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mcp-knowledge

by io.github.getgapup

earnings_reviewer

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Earnings Reviewer — Gapup agent-payable C-suite expertise (FUNDRAISING). Returns a structured, audited deliverable. Reference case: Salesforce Q3 FY2026 — call transcript + 10-Q + guidance → analyst note. Inputs are validated server-side — send the documented case fields.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
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.
companyYes
quarterYes
analystFocusNo
secFilingContextNo
transcriptExcerptYes

TDQS

B3.2/5.0
Behavior4/5

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

The annotations already declare readOnlyHint=true and openWorldHint=true, indicating no destructive effects and schema flexibility. The description adds that inputs are validated server-side and the tool returns a deliverable. This does not contradict any annotations and provides additional context about the tool's behavior.

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 short (three sentences) and front-loads the purpose. However, the phrasing 'Gapup agent-payable C-suite expertise (FUNDRAISING)' is jargon-heavy and may confuse agents. It earns its place but could be clearer.

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?

Despite the tool's complexity (6 parameters, nested objects, no output schema), the description does not explain what the deliverable looks like, how to use the async parameter, or provide detailed parameter semantics. Given the low schema description coverage, this leaves significant 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 description coverage is only 17%—only the 'async' parameter has a description. The description only says 'send the documented case fields', offering no explanation for the required parameters (company, quarter, transcriptExcerpt) or the optional ones (analystFocus, secFilingContext). This leaves agents without guidance on how to correctly populate nested objects.

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 returns a 'structured, audited deliverable' for earnings review, with a focus on fundraising. It provides a reference case (Salesforce Q3 FY2026) that illustrates the inputs and output. However, it does not explicitly distinguish this tool from the sibling 'earnings_transcript_signals', which may have overlapping functionality.

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 implies the tool is for fundraising contexts ('agent-payable C-suite expertise (FUNDRAISING)'), giving a use case hint. However, it does not specify when to use this tool versus alternatives, nor does it provide any when-not conditions or prerequisites.

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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