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MOAT (PAID -- AI): semantic retrieval over EMBEDDED SEC filings for a ticker. Ask a natural-language question ('what did they say about supply-chain risk?') and get the most relevant filing CHUNKS back from an Oxford Ledge hybrid retrieval (lexical BM25 plus a dense pass, fused by reciprocal rank). This is the ONE metered tool: it draws on your daily AI quota (25 searches/day, UTC reset, plus a burst limiter) -- set OXFORD_LEDGE_USER_ID + OXFORD_LEDGE_USER_TIER in your client config or the call fails AUTH_REQUIRED. Returns {summary, ticker, count, chunks, quota_remaining_today}; each chunk is {section, text (an excerpt), accession, url, score}. score is the FUSED RANK score (4dp), NOT a similarity: compare it only within one response. k default 5, hard cap 15. Source: SEC EDGAR filings (Oxford Ledge embedded corpus). Caveats ride the response's tool_notes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kNoNumber of chunks to return (default 5, hard cap 15).
queryYesNatural-language question to retrieve relevant filing passages for.
tickerYesStock ticker symbol (e.g. AAPL)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Annotations only declare readOnlyHint=true, but the description adds substantial behavioral context: the daily AI quota (25/day, UTC reset, burst limiter), required auth env vars and the AUTH_REQUIRED failure mode, the metered nature of the call, and the meaning of the fused score. This is exactly the value-add beyond structured fields that earns a 5.

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?

Front-loads purpose and the metered warning, then packs quota, auth, return shape, and score caveat efficiently. It is dense and repeats k's default/cap from the schema, which costs a point, but nearly every sentence carries needed information.

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?

There is no output schema, so the description must document returns, and it fully does so: top-level {summary, ticker, count, chunks, quota_remaining_today} and per-chunk {section, text, accession, url, score}, plus the critical caveat that score is a fused rank, not similarity. Nothing needed to call or interpret the result is missing.

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

Parameters3/5

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

Schema coverage is 100%, so the baseline is 3. The description restates k's default and cap already in the schema and adds usage context for query, but it does not extend parameter semantics beyond what the schema provides.

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?

States a specific verb and resource ('semantic retrieval over EMBEDDED SEC filings for a ticker') with a concrete example question. It also distinguishes itself from all siblings by flagging that it is 'the ONE metered tool', so an agent can place it without opening any other schema.

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

Usage Guidelines4/5

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

Clearly conveys when to use it (natural-language questions over filing text) and emphasizes the cost/quota dimension that selects it or not. It does not explicitly name a non-metered alternative sibling to use instead, so the routing guidance is strong but not fully closed.

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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