Signal8 MCP Serverofficial
A
licenseA
qualityD
maintenanceProvides AI agents with direct access to SEC filing intelligence, company fundamentals, dilution risk scoring, and cross-company analytics for financial research.
87
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1
MIT
openpitch-mcp
by Avierovich
The openpitch-mcp server provides a real-time intelligence layer for AI startups, letting you query, compare, and track sourced, confidence-scored data.
List companies – Browse the covered AI startup universe with headline metrics, filtering, and sorting options.
Get company profile – Retrieve a full profile for a specific company, including all resolved metrics and source provenance.
Get a specific metric – Fetch value/range, confidence score, estimate type, as-of date, and sources for one metric (e.g., ARR, valuation, funding), with optional history.
Inspect provenance – Drill into the underlying claims and confidence factors behind any metric to see exactly how it was derived.
Compare companies – Side-by-side metric comparison across multiple companies simultaneously.
What moved – Discover material changes, contradictions, and universe entries/exits since a given date, with optional confidence filtering.
Get events – Access a filtered event stream by type, company, date range, and minimum confidence score.
Search – Lexical search across company names, aliases, categories, and metric keys.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@openpitch-mcpWhat's the latest ARR for Anthropic?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
🪧 OpenPitch
The open, real-time intelligence layer for AI startups — that any agent can build on.
A free, open-source alternative to PitchBook & CB Insights, focused on the AI companies VCs actually care about.
MCP-native · zero-cost · fully-sourced · updated daily
Status: v0.1.3 — functional. The pipeline, reconciliation engine, MCP server, and dashboard all work end-to-end. Coverage and source breadth keep growing via the daily run.
Why OpenPitch exists
PitchBook and CB Insights cost $20k+/year — and for fast-moving AI startups, their data is often months stale, because human verification is slow. For a company growing 3× a year, a figure verified six months ago can be off by multiples.
Meanwhile, the real numbers are already public: founders state ARR on podcasts weeks before any database, funding hits SEC filings, hiring velocity reveals growth. They're just scattered, unstructured, and contradictory — exactly the problem an AI agent is built to solve.
OpenPitch's bet is latency, not coverage. For the AI companies that matter, a fresh, fully-sourced, confidence-scored number beats a verified-but-stale one. We don't claim certainty — we show you the receipts.
Related MCP server: NUVC MCP Server
What you get
Ask your coding agent, get an answer with receipts:
> what's Sierra's valuation, with sources?
Sierra — AI agents for customer service (sierra.ai)
Valuation $15.4B [consensus · confidence 0.96] · as of 2026-05
↳ 10 public sources · Reuters · CNBC · The Information · qz.com
↳ $950M round closed May 2026 — led by Tiger Global and GV(A real answer from the committed data — check it against the live dashboard.)
Every number carries its source, a confidence score, and a tracked history of how it changed.
Features
🎙️ Mines podcasts — founders leak metrics on podcasts before any database catches them. We transcribe and extract them.
🧾 Always sourced — every figure links to its origin (podcast timestamp, filing, article). No black-box numbers.
📊 Confidence-scored — built from source reliability, speaker authority, corroboration, and freshness (confidence decays as data ages).
🔀 Reconciles conflicts — when sources disagree, you get a consensus range + a contradiction flag, not a silent guess.
🧠 Learns which sources to trust — sources that prove right over time earn more weight.
🕒 Version-tracked — the git history is the audit log. See exactly how a company's reported ARR evolved.
📡 Composable — emits typed events other agents subscribe to (newsletters, press alerts, investor outbound).
🤝 A2A-discoverable — ships an A2A agent card so agent ecosystems can find and describe it.
🧯 Grounding — give your AI a sourced, confidence-scored fact base so it stops making up AI-company numbers.
⚡ 60-second install — no key, no signup; works in your agent in under a minute.
💸 Genuinely free — runs entirely on free tiers. No cost to run, no cost to use.
Quickstart — use it in Claude Code / Codex
No API key. No signup. No cost. The data is already built and committed; the MCP server just reads it, and your agent does the reasoning.
Fastest — zero install (reads the committed data from the public repo, no clone):
uvx openpitch-mcpOr install the package:
pip install openpitch # the MCP server (mcp is a core dependency)
openpitch-mcp # start the read-only serverOr run from a clone (for the pipeline / to rebuild data):
git clone https://github.com/Avierovich/openpitch && cd openpitch
python -m venv .venv && source .venv/bin/activate
pip install -e ".[pipeline]" # core + pipeline LLM deps
openpitch seed # build the data/ database from the committed seed (offline, no key)Then point your agent at the local server:
// MCP config (Claude Code / Codex) — zero-install via uvx:
{
"mcpServers": {
"openpitch": { "command": "uvx", "args": ["openpitch-mcp"] }
}
}
// (or "command": "openpitch-mcp" if you pip-installed the package)Ask your agent: "What's Cognition's ARR, with sources and confidence?" — it calls get_metric/get_provenance and answers from committed data (and will flag the public-source discrepancy).
Or just browse the data
🌐 Live dashboard — avierovich.github.io/openpitch (sourced company cards, refreshed daily) — or build locally:
openpitch build-dashboard📁 Raw data —
data/companies/— plain JSON, diffable, yours to use🤝 A2A Agent Card — generated at
dashboard/dist/.well-known/agent.json
Data status: live, refreshed daily by CI. Figures are probabilistic, public-source intelligence — every number carries its source, confidence score, and date, and open quality items are tracked in public. See the methodology and the correction workflow.
Docs
Trust model — methodology · data policy · corrections
Interfaces — MCP spec · events spec
Architecture — full design doc · more product docs in
docs/
How it works
Sources Daily pipeline (free GitHub Actions) Interfaces
────────── ─────────────────────────────────── ──────────
Podcasts ─┐ 1. select top-50 (VC-attention score) ┌─ MCP server (local, BYO agent)
News ─────┤ ───▶ 2. collect · 3. transcribe · 4. extract ───▶ ├─ static dashboard
SEC EDGAR ┤ 5. reconcile · 6. score sources ├─ event feed (JSONL)
Web ──────┘ 7. publish → git commit (the database) └─ "what moved today" digestThe git repo is the database. There's no server to run. See the FRD for the full design.
Build on it (composability)
OpenPitch emits typed, confidence-scored events when something material changes — so other agents can react:
You're building… | Subscribe to | OpenPitch becomes… |
A newsletter agent | all material events | your content pipeline's data source |
A press/PR workflow | funding/valuation events, confidence ≥ 0.8 | your "time to call the company" trigger |
Investor outbound | universe entries, growth thresholds | your targeting signal |
Events ship on MCP and a raw events/feed.jsonl. Schemas are versioned. See the events spec.
How we compare
OpenPitch is complementary to the incumbents, not a rip-and-replace. We win a narrow wedge; we lose on breadth and verification — and we're honest about both.
PitchBook / CB Insights | Crunchbase | Harmonic | MAGNiTT / Wamda | OpenPitch | |
Price | $20k–100k/yr | Freemium | Custom | $/regional | Free & open |
Freshness | Weeks–months | Variable | Days | Weeks | Daily |
In your AI agent (MCP) | ✗ | ✗ | ◐ | ✗ | ✓ |
Every figure sourced + confidence-scored | ◐ | ◐ | ◐ | ◐ | ✓ |
Contradiction detection | ✗ | ✗ | ✗ | ✗ | ✓ |
Coverage breadth | ✓✓✓ | ✓✓✓ | ✓✓ | ✓ (MENA) | narrow (by design) |
Verified, diligence-grade | ✓ | ◐ | ◐ | ◐ | ✗ (probabilistic) |
The honest pitch: the free, fresh, AI-native first look — every number sourced — before you pull the expensive verified report. For an investment decision, you still need the incumbents. Full mapping, feature matrix & pricing: docs/COMPETITIVE-ANALYSIS.md · spreadsheet.
Coverage
Global AI startups — 140+ profiled across 12 sectors (including Chinese AI labs and European names Western trackers miss), with a top 50 dynamically ranked by VC attention (valuation + funding activity — not ARR, to avoid circularity). The list moves as attention shifts; companies entering/leaving the top 50 is itself a tracked signal, and auto-discovery grows the universe daily.
MENA AI/tech segment — a dedicated regional set (an open, AI-native alternative to MAGNiTT/Wamda). Honest caveat: MENA disclosure is lighter than the US, so this segment launches with lower confidence/coverage, clearly labeled.
Seed universe: config/watchlist.yaml.
Honest disclaimer
OpenPitch is transparently probabilistic. Many figures are estimates derived from public, self-reported, sometimes-contradictory sources. We surface confidence and provenance precisely so you can judge for yourself. This is not investment advice, and figures are not guaranteed accurate. Always verify before acting.
Roadmap
Seed universe (global AI + MENA segment) + auto-discovery (news, funding digests, 21-sector backfill, China feed)
Core data model + reconciliation engine (confidence, consensus, contradiction) — tested
Source adapters: podcast, news, EDGAR, company-site — tested
Extraction stage: batched LLM claim extraction + model rotation — tested; data QA still required
MCP server — local read-only data tools
Daily GitHub Actions pipeline — wired for LLM, Groq transcription, and SEC user-agent secrets
Static dashboard + company pages — generated from committed data
Event feed — JSONL feed and digest generated from publishes
A2A agent discovery card — generated with dashboard
MENA adapters (regional news, free-zone registries)
Rich-source expansion (GitHub, hiring, app-ranks) — post-PMF scaling
v2: implied-ARR model, intra-day funding fast-lane
Contributing
Contributions welcome — especially new source adapters (one file each) and watchlist curation. See the FRD for architecture.
Who built this
OpenPitch is built and run by Mohamed Abdulhadi, a product manager — working with AI agents (Claude Code) that wrote much of the code and now operate the daily pipeline and its public data corrections. That's not a footnote; it's the product demonstrating itself: an agent-native database, built and maintained agent-natively, with every commit and correction in the open. Questions, feedback, or collaboration — connect on LinkedIn or open an issue.
License
Available Tools
8 toolscompare_companiesCRead-onlyIdempotent
Side-by-side metric comparison across companies.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| ids | Yes | ||
| metrics | Yes |
TDQS
C2.9/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. However, the description adds no behavioral context beyond what the annotations provide (e.g., rate limits, auth needs, or output format).
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 a single, concise sentence with no wasted words. It is front-loaded and efficiently conveys the core action.
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 absence of an output schema and the tool's moderate complexity (2 array parameters), the description is insufficient. It does not explain return values, parameter constraints, or expected behavior, leaving crucial gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning the input schema provides no details. The description does not explain the meaning or format of the 'ids' and 'metrics' parameters, leaving the agent to guess. For a tool with 0% coverage, the description must compensate, but it fails to do so.
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's purpose: 'Side-by-side metric comparison across companies.' It uses a specific verb ('compare'), identifies the resource ('companies'), and distinguishes it from sibling tools like get_company (single company) and get_metric (single metric).
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?
No guidance is provided on when to use this tool versus alternatives. The description merely states the action, leaving the agent to infer context. There are no explicit conditions, prerequisites, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_companyARead-onlyIdempotent
Full profile for one company: all resolved metrics with provenance.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | ||
| include_sources | No |
TDQS
A3.8/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate a safe, read-only, idempotent operation. The description adds that the result includes all metrics and provenance, but no further behavioral traits are disclosed.
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?
A single sentence that is efficient and front-loaded with key information. No redundant words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the tool's purpose and output content but lacks details on the include_sources parameter and the output structure (no output schema). Given the simplicity, it is moderately complete.
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?
The description does not explain the parameters; schema coverage is 0%. The id parameter's role is implicit from the tool name, but include_sources is not described, leaving its purpose unclear.
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 retrieves the full profile for one company including all resolved metrics with provenance. It distinguishes from sibling tools like list_companies and get_metric by specifying the scope and content.
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 implies use when a complete company profile is needed, but does not explicitly state when not to use it or mention alternative tools for partial data. The context is clear but lacks exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_eventsCRead-onlyIdempotent
Filtered event stream (the push layer).
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| type | No | ||
| since | No | ||
| company_id | No | ||
| min_confidence | No |
TDQS
C2/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, indicating safe read operations. The description adds no behavioral info beyond stating 'push layer', which is undefined and does not enhance transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely short (4 words), but this brevity sacrifices clarity and completeness. While concise, it fails to earn its place by providing necessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and zero schema description coverage, the description is grossly insufficient. It does not explain return values, pagination, or behavior, leaving major gaps for a tool with 4 parameters.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description provides no explanation of any parameter. Four parameters (type, since, company_id, min_confidence) are entirely undocumented, leaving the agent without meaning for filtering.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Filtered event stream (the push layer)' indicates it returns events with filtering capability, but it is vague and uses jargon ('push layer') without explanation. It somewhat distinguishes from sibling tools like search or get_company by focusing on events, but lacks specificity.
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?
No guidance on when to use this tool versus alternatives like search or what_moved. The description does not mention any context or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_metricCRead-onlyIdempotent
One metric with value/range, confidence, estimate_type, as_of, and sources.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| metric | Yes | ||
| company_id | Yes | ||
| with_history | No |
TDQS
C2.9/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, making the tool's safe read-only nature clear. The description adds the return fields (value/range, confidence, etc.), which is useful but does not disclose potential errors, rate limits, or performance impacts.
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 very short (one sentence), which is concise, but it sacrifices clarity and completeness. It is front-loaded with the main purpose, but the brevity leaves gaps.
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 has three parameters and no output schema, the description should explain the parameter effects and output structure more fully. It only lists return fields without connecting them to parameters, making it incomplete for an agent to use correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, yet the description does not explain the three parameters (metric, company_id, with_history). It only vaguely mentions the fields returned, leaving the agent without guidance on how to fill in parameters.
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 that the tool retrieves a single metric for a company, listing the fields returned. The name 'get_metric' aligns with the description, and it is well-distinguished from sibling tools like search or list_companies.
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?
No explicit guidance on when to use this tool versus alternatives. It doesn't mention that it's for individual metric retrieval or that search might be used for multiple metrics. No 'when not to use' or prerequisites provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_provenanceBRead-onlyIdempotent
Underlying claims + confidence factors behind a metric.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| metric | Yes | ||
| company_id | Yes |
TDQS
B3/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate the tool is readOnly, idempotent, and non-destructive. The description adds that it retrieves 'claims + confidence factors', which provides context beyond annotations, but does not detail any special behaviors like data freshness, ordering, or error conditions.
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 extremely concise at 6 words, front-loading the core concept. However, it sacrifices parameter and usage details, making it perhaps too terse. It earns its place but could be expanded without losing conciseness.
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 no output schema and 0% schema coverage, the description is incomplete. It explains the purpose but fails to provide usage guidelines, parameter semantics, or any details about return structure. This leaves significant gaps for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and the description does not explain the two parameters (metric, company_id). It implicitly references 'metric' but gives no details on allowed values, format, or relationship to other parameters. The description adds negligible value beyond the schema.
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 retrieves 'underlying claims + confidence factors' behind a metric, using a specific verb (get) and resource (provenance). This distinguishes it from sibling tools like get_metric (which gets the metric value) or what_moved (which shows changes).
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 provides no guidance on when to use this tool versus alternatives, nor does it mention prerequisites or limitations. Sibling tools exist but no explicit when-to-use or when-not-to-use advice is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_companiesCRead-onlyIdempotent
List covered AI companies with headline metrics.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| filter | No | ||
| segment | No | all | |
| sort_by | No | universe_rank |
TDQS
C2.7/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, so the description adds minimal behavioral context beyond 'headline metrics'. It does not mention return format, pagination, or data freshness, but the safety profile is clear from annotations.
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 very short and front-loaded, but too terse. It omits critical details, making it minimally adequate but not efficient for agent decision-making.
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 4 optional parameters with no output schema and multiple sibling tools, the description lacks necessary context about parameter behavior, return values, and differentiation from similar tools. The brevity leaves the agent underinformed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not explain any of the 4 parameters (limit, filter, segment, sort_by). Without additional text, the agent has no guidance on parameter meaning, format, or valid values beyond defaults.
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 specifies the verb 'List' and resource 'covered AI companies' with 'headline metrics', distinguishing it from siblings like 'get_company' (single company) and 'compare_companies' (comparison). However, it does not explicitly differentiate from 'search' or 'what_moved', leaving some ambiguity.
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 provides no guidance on when to use this tool versus siblings. No mention of prerequisites, exclusions, or context for appropriate usage, leaving the agent to infer from the name and sibling list alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
searchARead-onlyIdempotent
Lexical search over companies, aliases, categories, and metric keys.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes |
TDQS
A3.5/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate it is read-only (readOnlyHint: true), non-destructive, and idempotent. The description adds the behavioral trait 'lexical', meaning string-matching rather than semantic, but does not mention pagination, result limits, or return format.
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 a single sentence that conveys the core functionality without any wasted words. It is well-structured for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, read-only, no output schema), the description adequately covers the main purpose. However, it could mention that the search spans multiple entity types and any default behavior (e.g., case sensitivity).
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?
With 0% schema description coverage, the description must compensate, but it only vaguely links the query parameter to the search scope. It does not clarify expected format, example inputs, or behavior of the query parameter beyond the schema's minimal definition.
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 verb 'search' and the resources 'companies, aliases, categories, and metric keys', which is specific and distinguishes from sibling tools like get_company or list_companies that target individual resources.
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 provides no guidance on when to use this tool versus alternatives. It does not explain when a lexical search is appropriate compared to using get_company for exact matches or compare_companies for comparisons.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
what_movedCRead-onlyIdempotent
Material changes, contradictions, and universe entries/exits since a date.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| since | No | ||
| min_confidence | No | ||
| include_contradictions | No |
TDQS
C2.6/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only and idempotent behavior. Description adds only the temporal filtering ('since a date'), but discloses no additional behavioral traits such as data scope or impact.
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?
Single sentence, no wasted words, but at the cost of omitting important details. Adequately concise but not optimally structured.
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 3 optional parameters, no output schema, and no parameter documentation, the description fails to provide sufficient context about return values or parameter effects.
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 has 3 parameters with no descriptions (0% coverage). Description only implicitly references the 'since' parameter, omitting min_confidence and include_contradictions entirely.
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?
Description clearly states it lists material changes, contradictions, and universe entries/exits since a date, which distinguishes it from siblings like get_events and get_provenance. However, 'material changes' is somewhat vague.
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?
No explicit guidance on when to use this tool versus alternatives like get_events or get_provenance. The description does not mention when-not-to-use or provide context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
8 tool updates
v0.1.0- First observed
compare_companies - First observed
get_company - First observed
get_events - First observed
get_metric - First observed
get_provenance - First observed
list_companies - First observed
search - First observed
what_moved
TDQS
B3.3/5.0
Disambiguation5/5
Each tool has a clear, distinct purpose with no overlap. compare_companies for cross-company comparison, get_company for full profile, get_events for event stream, etc., all serve unique functions.
Naming Consistency5/5
All tool names use snake_case and follow a verb_noun pattern (e.g., get_company, list_companies). Even 'search' and 'what_moved' fit the pattern with imperative verbs or common query phrases.
Tool Count5/5
8 tools is well-scoped for an AI company data server. It provides comprehensive query, comparison, and change detection without being excessive or insufficient.
Completeness5/5
The tool set covers all essential operations for the domain: listing, detailed retrieval, metric queries, event streams, provenance, comparison, and change monitoring. No obvious gaps.
Maintenance
ActivityActive
ResponsivenessNo issues
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