gapup-mcp

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

Name	Required	Description	Default
`cik`	No
`async`	No	If 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.
`focus`	Yes		all
`ticker`	No
`filing_types`	Yes
`lookback_months`	Yes

TDQS

C2.8/5.0

Behavior3/5

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

The annotations already declare readOnlyHint=true and openWorldHint=true. The description adds that it returns a 'structured, audited deliverable' and validates inputs server-side, which provides some behavioral context. However, it does not elaborate on rate limits, authentication, or what 'audited' entails. With annotations covering the basics, this is adequate but not excellent.

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 relatively long and includes a list of example queries and a reference case. It is front-loaded with the purpose but lacks a clear structure (e.g., no separation of purpose, usage, parameters). Some sentences could be consolidated without losing clarity.

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 the tool's complexity (SEC filing analysis with multiple focus areas), the description does not fully explain the output format or what 'structured, audited deliverable' means. It lacks details on how results are presented, whether they include numerical data or narrative, and how to interpret them. The absence of an output schema increases the need for such context.

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 description coverage is only 17%, with only 'async' described. The description uses placeholders like <ticker> but does not explain each parameter's purpose or format. For example, 'focus' enum values are not elaborated. The description fails to compensate for the low schema coverage, leaving many parameters ambiguous.

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, specifically focusing on red flags, KPI movements, and executive summaries. It provides example queries that illustrate its purpose. However, it does not explicitly differentiate from sibling tools like earnings_reviewer or competitive_deep_dive, which could perform similar analyses.

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

Usage Guidelines2/5

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

The description includes example questions but provides no explicit guidance on when to use this tool versus alternatives. It does not mention when not to use it or suggest other tools for related tasks. The agent would need to infer usage from examples, which is insufficient for optimal selection.

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

C2.5/5.0

Disambiguation2/5

With 271 tools, many have overlapping purposes (e.g., multiple competitor intel tools, multiple financial modelers, multiple ESG auditors). Detailed descriptions help slightly, but the sheer volume creates confusion. Agents would struggle to select the right tool among many similar options.

Naming Consistency1/5

Tool names are wildly inconsistent: mix of English and French, snake_case and short phrases, some very generic (process, run, execute equivalents). No discernible naming convention (e.g., abm_architect vs. boundary_control vs. bp_narratif). This makes it hard to predict tool names.

Tool Count1/5

271 tools is far beyond typical well-scoped servers (3-15). This indicates an unfocused, over-bloated tool surface. Even for a general business intelligence server, this number is excessive and violates the principle of each tool earning its place.

Completeness2/5

Despite the large count, coverage feels scattered. Some domains (e.g., content, competitive intel) have many tools, while others (e.g., supply chain, HR) have gaps. The set lacks a coherent scope; it seems like a dump of many separate tool collections rather than a complete, curated surface.

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