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gapup-mcp

by io.gapup.mcp

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

A4.2/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds behavior such as automatic source fetching, failure handling (returns status:'failed' with explicit warning), multilingual support, and European ticker mapping. It does not contradict 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 well-structured with bullet points, front-loads purpose, and is informative without being overly verbose. Each sentence contributes value, though it could be slightly more concise.

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?

Given the tool's complexity (multiple outputs, source fetching, failure modes, multilingual), the description completely covers return values (signals, KPIs, guidance, Q&A, tone), failure behavior, and special features. No output schema exists, so the description adequately explains the expected outcomes.

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% with good parameter descriptions. The description adds marginal value by explaining European ticker mapping for company_or_ticker and transcript_text override, but does not significantly extend beyond schema for other parameters. 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 the tool extracts signals from earnings call transcripts for equity research, hedge funds, and BD teams. It lists specific outputs (signals, KPIs, guidance, Q&A topics, tone) and distinguishes itself from siblings by focusing on signal extraction from transcripts with automatic fetching, while other tools like earnings_reviewer likely have different scopes.

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?

The description defines target users and use cases (e.g., analyzing earnings transcripts for signals). It explains when to use transcript_text override and what happens when no transcript is found, but does not explicitly compare to sibling tools or state when not to use this tool.

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.

Resources