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99blakeD99

Metis FCA Handbook AI Harness MCP Server

by 99blakeD99

evaluate_fca_handbook_applicability

Identify which FCA Handbook rules apply to a specific firm, product, or service, providing compliance reasoning for authorisation, permissions, and obligations.

Instructions

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'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
user_inputYes
analysis_modeNoquick
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.

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