Skip to main content
Glama

mcp-knowledge

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

A4.6/5.0
Behavior5/5

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

Annotations include readOnlyHint=true, openWorldHint=true, and destructiveHint=false. The description adds significant detail: automatic fetching from specific sources (SEC EDGAR, Yahoo Finance, Motley Fool), behavior on failure (returns failed status with warning, never fabricates data), support for multilingual transcripts, European ticker mapping, and acceptable transcript_text override. No contradictions.

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

Conciseness5/5

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

The description is moderately long but well-structured with a clear introductory sentence, a bullet list of outputs, and concise sentences for additional behavior. Every sentence adds meaningful information; no 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?

Despite lacking an output schema, the description thoroughly describes the output structure (signals with priority levels, KPIs with YoY/QoQ%, guidance items, Q&A topics, tone). It also covers error states, multilingual support, and ticker mapping. The tool's complexity is fully addressed.

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

Parameters5/5

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

Schema coverage is 100% and the description enriches every parameter: lang affects mock transcript language, async returns job_id, quarter defaults, transcript_text skips fetches with min length, company_or_ticker includes examples and ADR mapping. This goes well beyond the schema alone.

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 is an 'Earnings call transcript signal extractor' and lists specific outputs (signals, KPIs, guidance, Q&A topics, tone). It also specifies automatic source fetching and handling of missing transcripts, distinguishing it from any similar sibling tools like earnings_reviewer.

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 identifies target users (equity research analysts, catalyst-driven hedge funds, BD teams) and outlines core functionality. However, it does not explicitly state when to prefer this tool over alternatives (e.g., earnings_reviewer) or provide when-not-to-use guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All mcp-knowledge toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.

Resources