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mcp-knowledge

by io.github.getgapup

sec_filing_decoder

Read-only

Décodeur de filing SEC — Gapup agent-payable C-suite expertise (CFO). Returns a structured, audited deliverable. Answers: Read the 10-K of and give me the material red flags, KPI movements, and a board-ready executive summary. · What has materially changed in 's risk profile in its latest annual filing? Flag any going-concern or auditor-change signals. · Is there any M&A signal or strategic review hint in 's most recent SEC filings? What's the evidence? · Prepare a due-diligence SEC filing brief for : financial snapshot, red flags, governance changes, and recommended next actions. · What is the sentiment of 's latest 10-K compared to its most recent 10-Q — bullish, neutral, or bearish? Reference case: SHOP · 10-K FY2024 · 4 red flags (1 critical: merchant concentration) · Revenue +24.7% YoY · . Inputs are validated server-side — send the documented case fields.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cikNo
asyncNoIf true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
focusYesall
tickerNo
filing_typesYes
lookback_monthsYes

TDQS

B3/5.0
Behavior3/5

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

Annotations indicate readOnlyHint=true and openWorldHint=true, which already signal safety and variability. The description adds that it 'returns a structured, audited deliverable' and that inputs are validated server-side. It does not contradict annotations, but adds little beyond purpose. No details on rate limits or response structure.

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

Conciseness3/5

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

The description is verbose, including multiple example questions and a reference case. The first sentence effectively states the purpose, but the subsequent examples could be more concise. Overall, it could be shortened without losing key information.

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

Completeness2/5

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

Given 6 parameters (3 required), no output schema, and limited schema descriptions, the tool description lacks completeness. It does not explain how to use cik vs ticker, the meaning of focus values, or the async behavior. The examples give some context but leave gaps for correct invocation.

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

Parameters2/5

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

Schema parameter description coverage is only 17%, meaning most parameters lack descriptions in the schema. The tool description does not explain parameters like cik, ticker, filing_types, lookback_months, or focus beyond example usage. It mentions ticker and filing types in examples but no explicit details on values or defaults. The meaning of async is not clarified in description (though schema has a description).

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

Purpose4/5

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

The description clearly states the tool decodes SEC filings, providing red flags, KPI movements, and executive summaries. It gives specific example questions like 'Read the 10-K of <ticker>...' and a reference case. However, it does not explicitly differentiate from sibling tools like earnings_reviewer or ma_deal_screener, though the focus on filing types (10-K, 10-Q) implicitly distinguishes it.

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

Usage Guidelines3/5

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

The description provides example queries that imply when to use the tool (e.g., analyzing SEC filings for red flags, M&A clues). It gives use cases but no explicit guidance on when not to use it or alternatives. There is no mention of prerequisites or limitations.

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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TDQS

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

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