Your agent + our methods → your Moat Map

Augment your agent.
Get your Moat Map.

Paste your agent's system prompt — or just describe what it does. We read it against what FoxAPIs can do, show you exactly where it's blind to real people and companies, the method that fills each gap, and an honest read on how deep a moat that builds.

No sign-up. Try: recruiting · sales · investing · content
Augmentation Map — where your agent is blind, and the method that fills it
Your Moat Map
Before / after — a real sample run
Without augmentation
With the recommended methods
Export — wire it into your agent
The map, moat read, and sample above are free. Export your ready-to-paste config:

Stage 3 — implement it in the builder

Wire these exact methods into your agent — pick the deep-moat methods first, add the included ones as free sweeteners. No code.

Open the builder ⚡

The wall agents hit

A capable agent can draft the outreach, schedule the call, and write the memo. The moment it has to judge a real human or company, it falls back on generic recall — and quietly guesses.

Without an evaluation layer

It guesses, confidently

Asked to screen an applicant, vet a co-investor, or check whether an influencer's following is real, a bare agent produces a plausible paragraph with no grounding, no sourcing, and no way to tell a strong match from a fabrication.

With FoxAPIs

It evaluates, with receipts

The same agent calls a tool, gets back a structured, sourced assessment — background, comparison, contactability, audience quality, buying intent — and reasons over evidence instead of vibes. Every claim carries its citation.

What agents use it for

Anywhere an agent has to decide whether a person or company is real, qualified, or worth the next step.

Hiring — evaluate & rank candidates

An agent screening inbound applicants runs a 30-dimension sourced scorecard on each candidate against the role, then ranks the shortlist side by side on evidence — before a recruiter spends a minute.

candidate_evaluate · candidate_compare

Compare executives & leaders

Sizing up a competitor's leadership bench or a leadership hire, an agent lines up 2–10 executives in a leadership frame on sourced evidence — so the read starts from facts, not vibes.

exec_compare · compare_subjects

Screen & vet a named person

An agent acting on a name first pulls a sourced screening dossier — history, affiliations, red flags — so it never acts on a fabrication. Cache hits are free.

vet_person · get_dossier

Verify investors & founders

First-pass diligence: an agent pulls a verified investor track-record snapshot or a deep founder due-diligence report, then compares subjects head to head, so the partner meeting starts from evidence.

get_investor_report · get_entrepreneur_report · compare_people

The deep-moat methods — lead with these

These are the methods that actually deepen your agent's moat: sourced, 30-dimension evaluation of real people and companies that a bare model cannot fake. Each is callable by name over the Model Context Protocol, and over plain HTTP via JSON-RPC against the same endpoint. Names resolve with built-in disambiguation: hand it a name and, if more than one person matches, it returns a candidate list at no charge so the agent locks in by id.

candidate_evaluate
Score one candidate against a role
Deep multi-source evaluation of a single candidate versus a role — a 30-dimension sourced scorecard the agent can defend line by line. Runs async (202 + poll), and is free when the person can't be found.
Inname or person_id; role / job context
Out30-dimension sourced scorecard, fit summary, citations, dossier URL
Costasync — 202 + poll /v1/jobs/{id}; free if unfindable
MCP tool · candidate_evaluate
HTTP · POST /v1/candidate_evaluate
candidate_compare
Rank 2–10 candidates for a role
Ranks a shortlist of candidates side by side in a hiring frame, on sourced evidence — the head-to-head an agent needs to recommend who to interview.
Incandidates[] (2–10); role / hiring context
Outranked rows, per-dimension values, recommended shortlist
Cost5 credits / candidate
MCP tool · candidate_compare
HTTP · POST /v1/candidate_compare
exec_compare
Compare 2–10 executives
Compares executives in a leadership / competitor-exec frame, on sourced evidence — for sizing up a competitor's bench or a leadership hire.
Inexecs[] (2–10); optional context
Outleadership-frame comparison rows, per-dimension values, citations
Cost5 credits / exec
MCP tool · exec_compare
HTTP · POST /v1/exec_compare
bulk_candidate_compare
Several comparisons, one call
Runs several candidate comparisons in a single call — one candidate set per open role — so an agent triaging many reqs gets every shortlist ranked at once.
Insets[] — each: role + candidates[]
Outone ranked comparison per role
Cost5 credits / subject, summed
MCP tool · bulk_candidate_compare
HTTP · POST /v1/bulk_candidate_compare
vet_person
Sourced screening dossier on a person
A sourced screening dossier on a person: history, affiliations, red flags — the canonical first step before an agent acts on a name. Cache hits return instantly; cold runs are async.
Inname or person_id; optional company hint
Outhistory, affiliations, red flags, populated sections, share URL
Cost30 credits cold / 0 on cache — cache sync, cold = 202 + poll
MCP tool · vet_person
HTTP · POST /v1/vet_person
get_dossier
Full sourced person dossier
The same backing as vet_person in a fuller dossier framing — career, network, reputation, public writing, recent news, key topics — every populated section in one structured object.
Inname or person_id; optional company
Outall enrichment sections, citations, share URL
Cost30 / 0 on cache · async
MCP tool · get_dossier
HTTP · POST /v1/get_dossier
compare_people
Rank 2+ people on 30 dimensions
Ranks people side by side across 30 dimensions, context-aware — communication style, career trajectory, skills depth, network density, press footprint and more.
Inpeople[] (2+, each name or person_id); optional context
Outdimensions[] — one row per dimension, a value per person
Cost5 credits / subject
MCP tool · compare_people
HTTP · POST /v1/compare_people
compare_subjects
Side-by-side of any 2–10 subjects
Context-aware side-by-side of any 2–10 real subjects — the same comparison engine as compare_people, exposed by its general-purpose name.
Insubjects[] (2–10, each name or id); optional context
Outdimensions[] — one row per dimension, a value per subject
Cost5 credits / subject
MCP tool · compare_subjects
HTTP · POST /v1/compare_subjects

More intelligence

Still deep, still sourced — investor, founder, sales and audience evaluation, plus the signal and contact-resolution methods your agent reaches for next.

get_investor_report
Verified investor / firm track record
A verified track-record snapshot on an investor or firm — for an agent doing first-pass diligence on a co-investor or a lead.
Inname or firm; optional context
Outtrack record, notable deals, focus, citations
Cost30 / free on thin data
MCP tool · get_investor_report
HTTP · POST /v1/get_investor_report
get_entrepreneur_report
Deep founder due-diligence
A deep founder due-diligence report — background, track record, signal — for the partner meeting that needs to start from evidence. Runs async.
Inname or person_id; optional company
Outfounder dossier, track record, citations
Costasync 202 + poll; 200 / free if unfindable
MCP tool · get_entrepreneur_report
HTTP · POST /v1/get_entrepreneur_report
generate_sales_dossier
Sourced pre-call sales dossier
A sourced pre-call sales dossier built from a company domain — so an agent walks into the call knowing the account.
Indomain; optional context
Outcompany overview, signals, talking points, citations
Cost30 credits
MCP tool · generate_sales_dossier
HTTP · POST /v1/generate_sales_dossier
analyze_influencer
Audience authenticity + brand fit
An audience-authenticity and brand-fit score with fraud flags — so spend follows real audiences, not inflated follower counts.
Inname, person_id, or handle; optional platform
Outauthenticity score, brand-fit notes, fraud flags, content themes
Cost30 basic / 50 full
MCP tool · analyze_influencer
HTTP · POST /v1/analyze_influencer
score_intent
Buyer-intent score of a post
Scores a social post for buyer intent on real signal and explains the score — so an agent watching the market acts on conversations that are actually ready to convert.
Inpost or topic; optional category, sources[]
Outintent_score, intent_reason, source_url
Cost2 credits
MCP tool · score_intent
HTTP · POST /v1/score_intent
find_contact
Resolve a person to a verified contact
Resolves a named person to a verified, reachable contact — so an agent's outreach step lands in a real inbox instead of a guess.
Inname or person_id; optional company
Outfull_name, company, title, verified email, profile URLs
Cost100 / 0 unfindable
MCP tool · find_contact
HTTP · POST /v1/find_contact

Included — table-stakes

These ship with every plan and are worth wiring up — but be honest: anyone can call them, and they don't deepen your moat. They run on capacity you've already paid for. The deep-moat methods above are what set your agent apart.

enrich_person
Person enrichment (light / deep)
Fills out a thin contact record — light or deep. Useful plumbing, but commodity: every data tool offers some version of this.
Inname or person_id; optional company, depth
Outfull_name, company, title, profile URLs, summary
MCP tool · enrich_person
HTTP · POST /v1/enrich_person
find_email
Find a verified email at a domain
Returns a verified email for a person at a domain. Table-stakes contact resolution — included, but it's not a moat.
Inname + domain
Outverified email, confidence
Cost100 / 0
MCP tool · find_email
HTTP · POST /v1/find_email
web & signal endpoints
Extraction + per-platform signal packs
Web extraction (/extract · /screenshot · /meta · /platform · /intent), the per-platform scrapers, hashtag / social / image-tag, and AI-visibility / brand signal packs. All included, all callable by anyone — sweeteners on top of the moat, not the moat itself. Full list in the docs.
Ina URL, handle, or query, per endpoint
Outstructured page / post / signal data
See · /docs — Included / table-stakes
HTTP · the documented per-platform paths

How to connect

FoxAPIs speaks the Model Context Protocol — the standard the agent ecosystem is converging on. Point any MCP-compatible client at the server and the whole evaluation catalog becomes callable in plain language.

# The MCP server
URL   https://www.mentionfox.com/mcp
Auth  Authorization: Bearer YOUR_FOXAPIS_KEY   # get one at /pricing
RPC   JSON-RPC 2.0 — methods: tools/list, tools/call

Claude Desktop

  1. Open claude_desktop_config.json.
  2. Add a foxapis entry under mcpServers with the URL and your Bearer key.
  3. Quit fully and reopen. The tools appear in the composer.

Cursor

  1. Open Cursor's MCP settings.
  2. Add a server pointing at /mcp with the Authorization header.
  3. Ask Cursor to call a tool by name, e.g. vet_person.

ChatGPT

  1. Add FoxAPIs as a connector / custom action.
  2. Use the server URL; authorize in the browser when prompted.
  3. The model selects tools from the catalog automatically.

n8n, Make & Zapier

  1. Use an HTTP Request step.
  2. POST JSON-RPC tools/call to /mcp with the Bearer header.
  3. Map the tool name and arguments from previous nodes.

GitHub Copilot SDK & ACP agents

  1. Register the FoxAPIs MCP server in your agent runtime.
  2. Provide the URL and credential once.
  3. The evaluation tools join your agent's tool list.

Any MCP client (OAuth)

  1. Point the client at the server URL.
  2. It discovers the auth endpoints and runs dynamic client registration automatically.
  3. Approve access in the browser — no key to paste.
// claude_desktop_config.json
{
  "mcpServers": {
    "foxapis": {
      "transport": "http",
      "url": "https://www.mentionfox.com/mcp",
      "headers": { "Authorization": "Bearer YOUR_FOXAPIS_KEY" }
    }
  }
}
# Call a tool over plain HTTP (JSON-RPC) — works from any language or no-code HTTP node
curl -X POST https://www.mentionfox.com/mcp \
  -H "Authorization: Bearer $FOXAPIS_KEY" \
  -H "Content-Type: application/json" \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/call",
       "params":{"name":"vet_person","arguments":{"name":"Jane Doe","company":"Acme"}}}'
Every result an evaluation tool returns is sourced and carries its citation — your agent reasons over evidence, and you can show the user where each claim came from. Ambiguous names never burn a call: the tool returns candidates at no charge so the agent can confirm the right person first.

Questions

Do I need new endpoints built for this?

No. The evaluation capability already exists and is live on the FoxAPIs MCP server. You connect your agent and start calling the tools — there is nothing to deploy on your side beyond adding the server.

Is this MCP-only, or can I use REST?

Both. The same endpoint answers JSON-RPC over HTTP, so any language or no-code HTTP node can call tools/call directly. MCP clients get the catalog and natural-language tool selection for free.

How does billing work?

FoxAPIs runs on its own credit system, separate from anything else. Per-call costs and packs are on the pricing page. Disambiguation candidate lists are returned at no charge.

Where do the results come from?

Each result is sourced and cited — the tools return the references behind every assertion, and flag low confidence rather than inventing detail. They are a fast, agent-callable first pass, not a regulated background check.