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gapup-mcp

by io.github.getgapup

earnings_transcript_signals

Read-only

Earnings call transcript signal extractor for equity research analysts, catalyst-driven hedge funds, and BD teams. Parses earnings transcripts (fetched or provided) to surface:

• signals (P0/P1/P2): guidance raise/cut, miss/beat vs consensus, buyback, dividend change, new product, executive change, capex shift, M&A intent, regulatory risk, competitive threat, supply chain, hiring • kpis_mentioned: Revenue, EBITDA, EPS, FCF, Gross Margin, Operating Margin with YoY/QoQ % • guidance: raised / maintained / cut / new_initiated items extracted • q_and_a_topics: top Q&A themes detected (AI strategy, China exposure, M&A pipeline, macro, etc.) • overall_tone: bullish / neutral / bearish

Sources fetched automatically: SEC EDGAR 8-K filings, Yahoo Finance earnings news, Motley Fool transcripts. If no transcript can be retrieved from any source, returns status:'failed' with an explicit warning and empty signals — never fabricated data. Accepts transcript_text override for direct analysis. Supports multilingual transcripts (de/fr/es/zh). European tickers (SAP.DE, BMW.DE) mapped to EDGAR-compatible equivalents automatically.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoLanguage hint for the transcript. Affects mock transcript language when fetch fails.
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.
quarterNoFiscal quarter in format Q1-2026. Defaults to the most recent past quarter.
transcript_textNoIf provided, skips all external fetches and analyses this text directly. Minimum 100 characters.
company_or_tickerYesCompany name or ticker symbol (e.g. 'Tesla', 'TSLA', 'SAP', 'SAP.DE', 'Sanofi', 'SNY'). European tickers (SAP.DE, BMW.DE) are mapped to their ADR equivalents for EDGAR lookup.

TDQS

A4.4/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds valuable behavioral details: 'never fabricated data', failure status with warning, automatic source fetch from SEC EDGAR/Yahoo/Motley Fool, multilingual support, and European ticker mapping. This goes well beyond the annotations.

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 fairly long but uses bullet points to organize the output items and clear paragraphs for sources and behavior. It is front-loaded with the purpose, and each sentence adds new information without redundancy.

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?

The tool is complex (5 params, multiple sources, output structure), and the description covers purpose, outputs, sources, failure handling, override, multilingual support, and ticker mapping. Since there is no output schema, the description adequately explains the return values and failure status.

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 description coverage is 100% and each parameter (lang, async, quarter, transcript_text, company_or_ticker) already has a clear schema description. The description adds minor context (e.g., European ticker mapping, language hint affects mock transcript) but largely relies on the schema, so baseline 3 is appropriate.

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 it is an 'earnings call transcript signal extractor' and enumerates the exact outputs (signals, kpis, guidance, q_a_topics, overall_tone). It distinguishes itself from generic financial tools by specifying the parsed items and sources.

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?

It identifies target users (equity research, hedge funds, BD) and describes when to use the transcript_text override and behavior when no transcript is found. However, it does not explicitly compare with sibling tools like earnings_reviewer, so it provides clear context but no exclusions.

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

C2.4/5.0
Disambiguation1/5

Over 50 tools share the identical template 'Gapup agent-payable C-suite expertise' with similar French descriptions and reference cases, making their boundaries indistinguishable. Clusters like competitor_intel, competitive_deep_dive, competitor_moves, competitor_profiles, competitor_pricing_radar, competitor_pricing_scrape, and competitor_recommendations heavily overlap in purpose.

Naming Consistency1/5

Names are chaotic: mix of French and English, snake_case and camelCase, verb_noun, noun, and adjective forms with no uniform pattern. Examples like 'bp_narratif', 'content_enrichment', 'ai_governance_full_report_async', and 'job_result' show no coherent naming convention.

Tool Count1/5

271 tools is far beyond any reasonable MCP server scope, creating an overwhelming selection burden for agents. This count vastly exceeds the 25+ threshold for 'too many' and makes navigation impractical.

Completeness2/5

While the server covers many business domains, it lacks lifecycle operations (e.g., no update/delete tools for the deliverables it generates) and the input specifications are vague ('documented case fields' without documentation), creating functional dead ends. The sheer breadth does not compensate for these gaps.

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