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Metis FCA Handbook AI Harness MCP Server

by 99blakeD99

Metis FCA Handbook AI Harness — MCP Server

An MCP (Model Context Protocol) server that integrates the Metis FCA Handbook AI Harness into your AI workflow. MCP is supported by Claude, OpenAI, Gemini, and most desktop/IDE MCP clients (Cursor, Windsurf, Cline, and others) — this README uses Claude Desktop as a fully worked example; adjust the configuration steps to fit your own client.

Source & full documentation: github.com/99blakeD99/the-metis-fca-handbook-ai-harness-mcp-files

Tools

evaluate_fca_handbook_applicability

Evaluate which FCA Handbook entries apply to an entity, via the Metis FCA Handbook AI Harness. One-shot: no session, no conversation state. Supports quick mode (60-120 seconds, default) and full mode (longer, more detailed). Returns a compliance report with verbatim citations, gaps, and refinement suggestions.

Related MCP server: eu-regulations

Why choose The Metis FCA Handbook AI Harness?

  • Efficiency Multiplies effectiveness of compliance advice. Saves £80+ in token fees per Harness run.

  • Deals with "Hard Problem", in which LLMs' token incentives prioritise training data so results are unreliable.

  • Verbatim citations Quotes verbatim entries from the FCA Handbook. Other AI systems struggle to do this.

  • Matches real-world need You do not have to start off knowing which sections you are looking for. Carries out structured searches across all 10,000+ FCA Handbook entries.

  • Secure Design Harness compartmentalisation, one-shot structure, and statelessness fits natively with emerging AI agent security standards such as OWASP Top 10 for Agentic Applications 2026.

  • AI Accessible Integrates easily with AI workflows and agents. Your LLM can use it as a tool.

Try the Harness Live

Experience the Harness interactively before integrating:

https://fcahandbookharnessimplementation.onrender.com

Email the-metis-fca-handbook-ai-harness@jbmd.co.uk and request a free temporary Access Code.

Ask compliance questions in natural language and watch each reasoning step unfold in real-time.

Design Principles

The Harness is built on proven principles:

  • Stateless — Each request is independent; no session coupling

  • One-shot — Complete analysis in a single call; no multi-turn state

  • Clear contract — Explicit input/output schemas for easy integration

  • Hosted — Single canonical source; no version drift or stale data

Use Cases

Compliance AI Platforms

Embed FCA reasoning as a service within your compliance platform. Users ask natural-language questions; your platform calls the Harness and presents structured reasoning.

AI Agent Workflows

Agents building compliance workflows can include FCA Handbook reasoning as a composed tool—no external API calls, just MCP configuration.

Compliance Review Automation

Integrate into document review or due-diligence pipelines. Automatically screen new rules against FCA applicability.

For Regulatory Screening

FS firms screening MCP servers will find:

  • Simple model: Stateless, no hidden state, no background jobs

  • Clear contract: Explicit input schema, output schema, error modes

  • Transparent updates: Version pinning; no automatic upgrades

  • Single point of failure: If the Harness is down, it is obvious; no cascading config issues

  • Audit-friendly: All calls logged centrally, not distributed

Quick Start

1. Get an API Key

Visit the Metis account dashboard:

https://fcahandbookharnessimplementation.onrender.com
  • Click "Create Account" (modal on the homepage)

  • Complete Stripe payment flow (accounts are paid)

  • Navigate to API Keys and generate a new key

  • Save it securely

2. Install the MCP Server

pip install fca-handbook-harness-mcp

3. Configure your MCP client

This section walks through Claude Desktop as a fully worked example. The mcpServers JSON shape below is shared by most desktop/IDE MCP clients (Claude Code, Cursor, Windsurf, Cline, and others) — but the config file location and restart step are Claude Desktop's specifically. If you are using a different client, including one with a GUI-based connector flow (some OpenAI and Gemini integrations work this way) rather than a JSON config file, consult that client's own documentation for where to add a server.

Claude Desktop file location:

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

  • Windows: %APPDATA%\Claude\claude_desktop_config.json

  • Linux: ~/.config/Claude/claude_desktop_config.json

Add this server entry (create the file if it doesn't exist):

{
  "mcpServers": {
    "fca-handbook-harness": {
      "command": "fca-handbook-harness-mcp",
      "env": {
        "METIS_API_KEY": "sk_live_..."
      }
    }
  }
}

Replace sk_live_... with your actual API key from your Metis account.

4. Restart your MCP client

For Claude Desktop: quit and restart the app. Other clients reload MCP connections differently — check your client's documentation if unsure. Once connected, the evaluate_fca_handbook_applicability tool will be available in agent workflows.

Using the Tool

The tool accepts two parameters:

  • user_input (required, max 5000 characters): Everything together as one piece of text — firm type, products/services, target market, regulatory question, etc.

  • analysis_mode (optional): "quick" (default, ~60-120 seconds) or "full" (longer, detailed conditional reasoning)

The tool returns:

  • summary: 2-3 sentence overview of applicability

  • entry_analysis: Retrieved FCA Handbook entries with reasoning

  • obligations: High-confidence, conditional, and low-confidence obligations

  • gaps: What the analysis couldn't determine from your input

  • refinement_suggestions: Follow-up information that would improve accuracy

  • citations: Verbatim quotes from FCA Handbook with binding levels (R=Rule, G=Guidance)

  • tokens: Token count for cost/complexity tracking

Troubleshooting

Tool not appearing (Claude Desktop; the same class of issue applies to most desktop MCP clients):

  • Verify the config file path (platform-specific, see above)

  • Confirm fca-handbook-harness-mcp resolves on the command line (which fca-handbook-harness-mcp / where fca-handbook-harness-mcp). Desktop MCP clients typically launch with a minimal environment and may not see the same PATH as your shell — if the command does not resolve, replace "command": "fca-handbook-harness-mcp" with the absolute path from that lookup

  • Restart your MCP client (not just reload)

401 Unauthorized:

  • Verify METIS_API_KEY is set in the config env

  • Check the key is correct (copy from dashboard again)

  • Ensure no extra spaces or newlines in the key

Connection timeout:

  • The analysis can take 60-120 seconds (quick mode) or longer (full mode)

  • Ensure you have internet access to fcahandbookharnessimplementation.onrender.com

Files

  • mcp_server.py — MCP server implementation (main entry point; its docstring is the source of truth for the tool schema)

  • pyproject.toml — pypi package manifest

  • server.json — MCP registry manifest (registry.modelcontextprotocol.io format)

  • manifest.json — MCPB/Smithery bundle manifest

  • requirements.txt — Python dependencies (mcp, requests)

  • uv.lock — Pinned dependency resolution for reproducible uv run

  • __init__.py — Python package marker

  • README.md — This file

  • LICENSE — MIT License

  • .gitignore — Git ignore rules

  • .mcpbignore — Files excluded from the MCPB bundle

Support

For questions or issues, contact: the-metis-fca-handbook-ai-harness@jbmd.co.uk


Product: Metis FCA Handbook AI Harness
License: MIT

Available Tools

1 tool
evaluate_fca_handbook_applicabilityA

Evaluate which FCA Handbook entries apply to an entity, via the Metis FCA Handbook AI Harness.

Calls a live compliance reasoning run, billed to the configured account. Use for questions about FCA authorisation, permissions, or obligations for a specific firm/product/service.

CRITICAL: This call takes 90+ seconds to complete and streams progress messages. You MUST display each progress message to the user as it arrives. Do NOT wait silently for the result. Silently waiting makes the user think the tool is broken. The progress messages are not filler — they contain essential detail about what the Harness is doing (which reasoning node is running, what it found, etc.). Your response MUST actively incorporate and relay each message, not just the final result.

Before calling: check whether you already have (from this conversation, documents you were given, or other tools) grounded answers to these six things — the specific compliance question, the product/service, who's providing it (platform/adviser/bank/etc.), its key features, the target market (retail/institutional/professional), and what data it handles. If you are missing more than one or two, ask the user for them first rather than calling with thin input. Every call is billed to the account at a flat rate regardless of input quality, so a vague call followed by a refinement round costs twice what one good call would have.

The result includes refinement_suggestions — gaps the Harness couldn't resolve from user_input alone, typically subtler than the six basics above (e.g. a regulatory edge case, not a missing fact you could have just asked for). If you already have grounded information addressing one (from this conversation, documents you were given, or other tools you've called), fold it into a new user_input and call again yourself, rather than just relaying the suggestion to the user as a question. Do not speculate or infer plausible-sounding detail you do not actually have to fill a gap — that reintroduces the hallucination risk this Harness exists to avoid, one level up. Only ask the user for whatever's left that you genuinely do not know.

Args: user_input: Everything together as one piece of text (up to 5000 characters) — the specific compliance question, the product/service, who's providing it, its key features, the target market, and what data it handles. See "Before calling" above for why all six matter. analysis_mode: 'quick' (default, ~60-120 seconds) for a fast pass, or 'full' (longer) for detailed conditional reasoning — conditions, interactions between rules, and second-order implications. Ask the user which they want if it is not obvious; default to 'quick'.

ParametersJSON Schema
NameRequiredDescriptionDefault
user_inputYes
analysis_modeNoquick

TDQS

A4.9/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description fully discloses critical behaviors: the 90+ second runtime, streaming of progress messages, billing implications, the need to display progress messages, the nature of refinement_suggestions, and the explicit warning against speculation to avoid hallucination risk. This goes far beyond any annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but every section serves a purpose: purpose, critical warnings, pre-call guidance, parameter details, and post-call handling. It is well-structured with clear headings and critical caveats in block caps. It could be slightly condensed, but the length is justified by the complexity of behavioral quirks and usage requirements.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity, lack of annotations, and absence of an output schema, the description covers virtually all necessary aspects: purpose, when to use, what to prepare, runtime behavior, progress messaging, billing, refinement_suggestions handling, and detailed parameter semantics. It even addresses hallucination risk and when to ask the user vs. re-call. No major gaps are evident.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage, but the description thoroughly compensates. For 'user_input', it explains the content, up to 5000 characters, and why all six elements matter. For 'analysis_mode', it explains the 'quick' vs 'full' options, time ranges, what 'full' does, and advises asking the user if unclear. It adds substantial meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Evaluate which FCA Handbook entries apply to an entity' with a specific verb (Evaluate), resource (FCA Handbook entries), and scope (apply to an entity). It also names the specific system ('Metis FCA Handbook AI Harness'), which distinguishes it from any potential similar tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use the tool ('Use for questions about FCA authorisation, permissions, or obligations for a specific firm/product/service') and provides a detailed pre-call checklist of six required inputs, including when to ask the user for missing information. It also gives guidance on when to re-call the tool after receiving refinement_suggestions, covering both usage context and exclusions for thin input.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 1 tool updatev0.1.7
    • First observedevaluate_fca_handbook_applicability

TDQS

A4.7/5.0
Disambiguation5/5

Only one tool exists, so there is no possibility of confusion with other tools. Its purpose is clearly defined for evaluating FCA handbook applicability.

Naming Consistency5/5

The single tool follows a clear verb_noun pattern ('evaluate_fca_handbook_applicability'). With only one tool, there are no naming inconsistencies to penalize.

Tool Count3/5

The server exposes only one tool, which feels thin for a compliance-focused service. While the tool is substantial, a single tool offers minimal surface area and would benefit from auxiliary operations like retrieving past evaluations.

Completeness4/5

The sole tool thoroughly covers the advertised purpose of evaluating FCA handbook applicability, including detailed input guidance and refinement suggestions. However, it lacks any supporting operations such as listing or retrieving historical evaluations, leaving minor gaps for multi-step workflows.

Maintenance

ActivityMaintained
ResponsivenessNo issues

Resources

Unclaimed servers have limited discoverability.

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