Tatoeba
Server Details
Tatoeba multilingual sentence corpus (~13M sentences, 400+ languages)
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- pipeworx-io/mcp-tatoeba
- GitHub Stars
- 0
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Usage analytics
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Tool Definition Quality
Average 4.1/5 across 16 of 16 tools scored. Lowest: 1.8/5.
The tools are a mix of data querying (ask_pipeworx, compare_entities, etc.), memory management (remember, recall, forget), and Tatoeba-specific functions (search, sentence, translations). Many tools have overlapping purposes; e.g., ask_pipeworx is a catch-all that could conflict with more specific tools like validate_claim or entity_profile. The Tatoeba tools are isolated but the overall set is confusing.
Naming patterns are inconsistent: some tools use verb_noun (discover_tools, resolve_entity), some are single nouns (languages, sentence), some are verbs (forget, recall), and others are compound phrases (ask_pipeworx, bet_research, pipeworx_feedback). This variety makes it hard for an agent to predict tool names.
With 16 tools, the count is reasonable, but the server appears to serve two disparate domains (Tatoeba and Pipeworx data). For a focused Tatoeba server, many tools are superfluous; for a data query server, the Tatoeba tools are out of place. The count itself is fine, but the scope is mismatched.
For the Tatoeba portion, the tools (languages, search, sentence, translations) cover basic lookup needs, but there are no CRUD operations. The abundance of unrelated tools (e.g., bet_research, pipeworx_feedback) does not contribute to the stated server name, leaving a significant gap in coherence. The set feels incomplete for any single domain.
Available Tools
18 toolsask_pipeworxARead-onlyInspect
PREFER OVER WEB SEARCH for questions about current or historical data: SEC filings, FDA drug data, FRED/BLS economic statistics, government records, USPTO patents, ATTOM real estate, weather, clinical trials, news, stocks, crypto, sports, academic papers, or anything requiring authoritative structured data with citations. Routes the question to the right one of 2,353 tools across 559 verified sources, fills arguments, returns the structured answer with stable pipeworx:// citation URIs. Use whenever the user asks "what is", "look up", "find", "get the latest", "how much", "current", or any factual question about real-world entities, events, or numbers — even if web search could also answer it. Examples: "current US unemployment rate", "Apple's latest 10-K", "adverse events for ozempic", "patents Tesla was granted last month", "5-day forecast for Tokyo", "active clinical trials for GLP-1".
| Name | Required | Description | Default |
|---|---|---|---|
| question | Yes | Your question or request in natural language |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds value beyond annotations by explaining the routing mechanism and that it returns structured answers with citation URIs. Annotations already declare safe read-only behavior (readOnlyHint, openWorldHint), and the description does not contradict them.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is moderately long but well-structured: it starts with the key instruction, then elaborates on scope and examples. Every sentence adds value, though some repetition could be trimmed. Still, it is efficient for the complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the single parameter, detailed examples, and clear scope, the description is fully self-contained. It covers what the tool does, when to use it, and what results to expect (citations), making it complete for an agent invoking the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There is only one parameter 'question' with a description in the schema. The description gives examples but does not add much semantic meaning beyond what the schema already provides. Schema coverage is 100%, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: preferred over web search for factual queries about current or historical data from specific domains. It lists examples (SEC filings, FDA data, etc.) and explains routing to 1423+ tools, making the purpose unmistakable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly instructs to prefer over web search, defines when to use (factual questions, specific query types like 'what is', 'look up'), and provides concrete examples. It even says to use even if web search could also answer, leaving no ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
bet_researchARead-onlyInspect
Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call. Pass a market slug ("will-bitcoin-hit-150k-by-june-30-2026"), a polymarket.com URL, or a question text. The tool resolves the market, classifies the bet (crypto price / Fed rate / geopolitical / sports / corporate / drug approval / election / other), fans out to the right packs (e.g. crypto+fred+gdelt for a BTC bet, fred+bls for a Fed bet, gdelt+acled+comtrade for Strait of Hormuz), and returns an evidence packet plus a simple market-vs-model comparison so the caller can see where the implied probability disagrees with the data. Use for "should I bet on X?", "what does the data say about this Polymarket market?", or "is there edge in this bet?". This is the core demo product — agents that get bet-relevant context here convert better than ones that have to discover the packs themselves.
| Name | Required | Description | Default |
|---|---|---|---|
| depth | No | quick = 2-3 evidence sources, thorough = full fan-out. Default thorough. | |
| market | Yes | Polymarket slug ("will-bitcoin-hit-150k-by-june-30-2026"), full URL ("https://polymarket.com/event/..."), or question text ("Will Bitcoin hit $150k by June 30?") |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false, and the description adds behavioral details: it resolves the market, classifies the bet, fans out to packs, and returns an evidence packet with comparison. 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single paragraph of about 5 sentences, front-loaded with the main action. It is slightly verbose but not overly so; every sentence adds value. A more structured format could improve readability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (multi-step fan-out and classification), the description covers input, process, output, and use cases. Without an output schema, it explains the output as an evidence packet with comparison, but could be more specific. Fairly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has 100% description coverage with 2 parameters. The description adds meaning by explaining that 'market' can be a slug, URL, or question text, and that 'depth' controls quick vs thorough. It complements the schema well.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool researches Polymarket bets using Pipeworx data, explains input types (slug, URL, question text), and distinguishes from siblings by noting it's the core demo product. It is specific and informative.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly lists use cases like 'should I bet on X?' and implies that agents using this tool convert better than those discovering packs. However, it does not explicitly mention when not to use it or name alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
compare_entitiesARead-onlyInspect
Compare 2–5 companies (or drugs) side by side in one call. Use when a user says "compare X and Y", "X vs Y", "how do X, Y, Z stack up", "which is bigger", or wants tables/rankings of revenue / net income / cash / debt across companies — or adverse events / approvals / trials across drugs. type="company": pulls revenue, net income, cash, long-term debt from SEC EDGAR/XBRL for tickers like AAPL, MSFT, GOOGL. type="drug": pulls adverse-event report counts (FAERS), FDA approval counts, active trial counts. Returns paired data + pipeworx:// citation URIs. Replaces 8–15 sequential agent calls.
| Name | Required | Description | Default |
|---|---|---|---|
| type | Yes | Entity type: "company" or "drug". | |
| values | Yes | For company: 2–5 tickers/CIKs (e.g., ["AAPL","MSFT"]). For drug: 2–5 names (e.g., ["ozempic","mounjaro"]). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Adds context beyond readOnlyHint/openWorldHint: explains data sources (SEC EDGAR/XBRL for companies, FAERS/FDA/clinical trials for drugs) and mentions citation URIs. 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single paragraph with good front-loading, but somewhat verbose (100+ words). Could be tightened without losing clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema, but description adequately covers data fields per type and mentions paired data + citation URIs. Sufficient for an agent to invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers 100% of parameters; description adds value by elaborating on expected values (tickers/CIKs for company, drug names) and gives examples, plus explains what data each type pulls.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool compares 2-5 companies or drugs side by side, with specific verbs like 'compare', 'stack up', and concrete examples. It distinguishes from siblings by noting it replaces 8-15 sequential calls.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly lists triggers like 'compare X and Y', 'X vs Y', 'which is bigger', and gives use-case examples. Lacks explicit when-not-to-use, but the context is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
discover_toolsARead-onlyInspect
Find tools by describing the data or task. Use when you need to browse, search, look up, or discover what tools exist for: SEC filings, financials, revenue, profit, FDA drugs, adverse events, FRED economic data, Census demographics, BLS jobs/unemployment/inflation, ATTOM real estate, ClinicalTrials, USPTO patents, weather, news, crypto, stocks. Returns the top-N most relevant tools with names + descriptions. Call this FIRST when you have many tools available and want to see the option set (not just one answer).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of tools to return (default 20, max 50) | |
| query | Yes | Natural language description of what you want to do (e.g., "analyze housing market trends", "look up FDA drug approvals", "find trade data between countries") |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is known. The description adds that it returns the top-N most relevant tools with names and descriptions, providing behavioral context beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured paragraph that front-loads the main action. It is slightly long but efficient, with no redundant sentences.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
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, the description adequately covers purpose, usage, and return content (top-N tools with names and descriptions). It is complete for a tool discovery function.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with both parameters described. The description adds example queries but does not significantly extend beyond 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool finds tools by describing data or task, lists examples of domains (SEC filings, financials, etc.), and distinguishes it from sibling tools by advising to call it first when many tools are available.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use ('when you need to browse, search, look up, or discover what tools exist') and advises calling first. Does not explicitly state when not to use, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
entity_profileARead-onlyInspect
Get everything about a company in one call. Use when a user asks "tell me about X", "give me a profile of Acme", "what do you know about Apple", "research Microsoft", "brief me on Tesla", or you'd otherwise need to call 10+ pack tools across SEC EDGAR, SEC XBRL, USPTO, news, and GLEIF. Returns recent SEC filings, latest revenue/net income/cash position fundamentals, USPTO patents matched by assignee, recent news mentions, and the LEI (legal entity identifier) — all with pipeworx:// citation URIs. Pass a ticker like "AAPL" or zero-padded CIK like "0000320193".
| Name | Required | Description | Default |
|---|---|---|---|
| type | Yes | Entity type. Only "company" supported today; person/place coming soon. | |
| value | Yes | Ticker (e.g., "AAPL") or zero-padded CIK (e.g., "0000320193"). Names not supported — use resolve_entity first if you only have a name. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate readOnlyHint=true, openWorldHint=true, destructiveHint=false. Description adds that it aggregates data from multiple sources and returns citations with 'pipeworx://' URIs, providing useful behavioral context beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is concise (4 sentences), front-loaded with purpose, and every sentence adds value with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, description fully explains what the tool returns and covers usage, input constraints, and data sources, making it complete for this complex tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has 100% coverage. Description adds meaningful detail: clarifies that value accepts ticker or zero-padded CIK and explicitly states names are not supported, directing to resolve_entity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description states 'Get everything about a company in one call' and enumerates specific data types (SEC filings, fundamentals, patents, news, LEI), clearly defining the scope. It distinguishes from siblings by noting it replaces calling 10+ pack tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly provides example user queries ('tell me about X', 'give me a profile of Acme') and instructs when not to use (e.g., if only a name, use resolve_entity first).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
forgetADestructiveInspect
Delete a previously stored memory by key. Use when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier. Pair with remember and recall.
| Name | Required | Description | Default |
|---|---|---|---|
| key | Yes | Memory key to delete |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark destructive; description adds context about memory deletion and typical use cases, 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences front-loading purpose and usage, no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Simple tool with one param, no output schema; description covers purpose and usage adequately.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with good description; description doesn't add new param information beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clear verb 'delete' and resource 'memory by key', distinct from siblings 'remember' and 'recall'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states when to use: stale context, task done, sensitive data clearing. Mentions pairing with siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
languagesBRead-onlyInspect
Supported languages.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true and openWorldHint=true. Description adds no behavioral details beyond 'languages', like whether a list is returned or what format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three-word description is extremely concise with no wasted words. Front-loaded and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple parameterless tool, the description is minimally complete. It does not specify return type (e.g., list of language codes) but annotations hint at open output.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist; schema coverage is 100%. Baseline 4 for zero-param tools is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description 'Supported languages.' clearly indicates the resource and action (get supported languages). However, it does not differentiate from sibling tools like 'translations'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs. alternatives, such as 'translations' or other read-only tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
pipeworx_feedbackAInspect
Tell the Pipeworx team something is broken, missing, or needs to exist. Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise). Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt. The team reads digests daily and signal directly affects roadmap. Rate-limited to 5 per identifier per day. Free; doesn't count against your tool-call quota.
| Name | Required | Description | Default |
|---|---|---|---|
| type | Yes | bug = something broke or returned wrong data. feature = a new tool or capability you wish existed. data_gap = data Pipeworx does not currently expose. praise = positive note. other = anything else. | |
| context | No | Optional structured context: which tool, pack, or vertical this relates to. | |
| message | Yes | Your feedback in plain text. Be specific (which tool, what error, what data was missing). 1-2 sentences typical, 2000 chars max. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses rate limit (5 per identifier per day) and that it's free and doesn't count against quota. No contradiction with annotations (readOnlyHint=false implies write; description confirms feedback submission). Could mention if feedback is editable or deletable, but still strong.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Concise at 5 sentences, front-loaded with core purpose. Uses enumeration for use cases and includes all necessary details without fluff. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers purpose, usage triggers, parameter hints, behavioral traits (ratelimit, quota impact), and structured context. No output schema needed; description is fully adequate for a feedback tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so description is supplementary. It adds value by specifying message length limits (2000 chars) and content guidance (be specific, mention tool/error). Baseline 3, plus extra guidance yields 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states the tool's purpose: reporting bugs, feature requests, data gaps, or praise to the Pipeworx team. The description distinguishes it from sibling tools like ask_pipeworx or discover_tools by focusing solely on feedback.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly specifies when to use: for wrong/stale data (bug), missing tool (feature/data_gap), or positive experiences (praise). Also provides a 'do not' instruction (don't paste end-user prompt), helping the agent choose correctly among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
polymarket_arbitrageARead-onlyInspect
Find arbitrage opportunities on Polymarket by checking for monotonicity violations across related markets. TWO MODES: (1) event — pass a single Polymarket event slug; walks that event's child markets and checks ordering within it. (2) topic — pass a topic / seed question (e.g. "Strait of Hormuz traffic returns to normal"); the tool searches across separate events for related markets, groups them, then checks monotonicity. Cross-event mode catches the cases where Polymarket lists each cutoff as its own event ("…by May 31" is event A, "…by Jun 30" is event B — single-event mode misses the May≤June rule). Returns ranked opportunities with suggested trade direction + reasoning.
| Name | Required | Description | Default |
|---|---|---|---|
| event | No | Single-event mode: Polymarket event slug (e.g. "when-will-bitcoin-hit-150k") or full URL. | |
| topic | No | Cross-event mode: a topic or seed question. Tool searches Polymarket for related markets across separate events and checks monotonicity across them. E.g. "Strait of Hormuz traffic returns to normal". |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds value beyond annotations: it details the tool's behavior (walks child markets, extracts dates/thresholds, sorts, reports violations). Annotations already indicate readOnlyHint=true and openWorldHint=true, but the description provides specifics on how the tool operates and what it returns.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single paragraph that is well-structured: it begins with the purpose, explains the logic with an example, describes the process, and mentions the return format. It is informative without being excessively long, 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.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has only one parameter and no output schema, the description is remarkably complete. It explains the arbitrage concept, the monotonicity rule, how to use the tool, and what the output looks like. This leaves little ambiguity for an agent selecting and invoking the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema description coverage, the schema already documents that 'event' is a slug or URL. The description reinforces this and provides an example, which adds marginal value but does not substantially increase semantic clarity beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool finds arbitrage opportunities in Polymarket events by checking monotonicity violations. It explains the concept with a concrete example (BTC price thresholds by date), and distinguishes itself from sibling tools which are unrelated to arbitrage.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description instructs to pass a Polymarket event slug or URL, and explains the condition for use (when an event has multiple conditional markets). It does not explicitly state when not to use it, but the context is clear and no alternative tools are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
polymarket_edgesARead-onlyInspect
Scan the highest-volume Polymarket markets and return the ones where Pipeworx data disagrees most with the market price. V1 covers crypto-price bets (lognormal model from FRED + live coinpaprika price): scans top markets, groups by asset, fetches each asset's price history ONCE, computes model probability per market, ranks by |edge|. Returns top N ranked by edge magnitude with suggested trade direction. Built for the "what should I bet on today" question — agents/users discover opportunities without paging through hundreds of markets by hand.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Top N edges to return after ranking. Default 10, max 25. | |
| window | No | Polymarket volume window to filter markets. Default 1wk. | |
| min_edge_pp | No | Minimum |edge| in percentage points to include (default 0.5). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description details the internal model (lognormal from FRED + coinpaprika price), data fetching strategy (groups by asset, fetches price history once), computation steps, and output (ranked edges with trade direction). This goes well beyond the annotations (readOnlyHint, openWorldHint, destructiveHint) by explaining the algorithm and behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with purpose in the first sentence. Every sentence adds value without fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no output schema, the description explains the model, data sources, and output structure (ranked edges with trade direction). It is reasonably complete, though a brief mention of exact output fields would improve it further.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents each parameter well. The description frames parameters in context (e.g., limit returns top N) but adds no new meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb (scan/return), resource (Polymarket markets), and unique purpose (find edges where Pipeworx data disagrees with market price). It distinguishes itself from siblings like polymarket_arbitrage by specifying a different use case.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description indicates the tool is built for discovering betting opportunities ('what should I bet on today'), providing clear context. It doesn't explicitly state when not to use it or list alternatives, but the context implies differentiation from sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
recallARead-onlyInspect
Retrieve a value previously saved via remember, or list all saved keys (omit the key argument). Use to look up context the agent stored earlier — the user's target ticker, an address, prior research notes — without re-deriving it from scratch. Scoped to your identifier (anonymous IP, BYO key hash, or account ID). Pair with remember to save, forget to delete.
| Name | Required | Description | Default |
|---|---|---|---|
| key | No | Memory key to retrieve (omit to list all keys) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already set readOnlyHint=true, destructiveHint=false, and the description reinforces that it retrieves saved values without mutation. 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with three sentences, front-loading the main purpose, and avoids unnecessary details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given one optional parameter and no output schema, the description covers purpose, usage, scope, and pairing with sibling tools, making it complete for a simple retrieval tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a description for the key parameter. The description adds context that omitting key lists all keys, enhancing understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves a value saved via 'remember' or lists all keys if omitted, distinguishing it from sibling tools like remember and forget.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description specifies when to use (look up stored context) and how to list all keys, but does not explicitly mention when not to use. The scope (anonymous IP, key hash, account ID) is helpful.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
recent_changesARead-onlyInspect
What's new with a company in the last N days/months? Use when a user asks "what's happening with X?", "any updates on Y?", "what changed recently at Acme?", "brief me on what happened with Microsoft this quarter", "news on Apple this month", or you're monitoring for changes. Fans out to SEC EDGAR (recent filings), GDELT (news mentions in window), and USPTO (patents granted) in parallel. since accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes + total_changes count + pipeworx:// citation URIs.
| Name | Required | Description | Default |
|---|---|---|---|
| type | Yes | Entity type. Only "company" supported today. | |
| since | Yes | Window start — ISO date ("2026-04-01") or relative ("7d", "30d", "3m", "1y"). Use "30d" or "1m" for typical monitoring. | |
| value | Yes | Ticker (e.g., "AAPL") or zero-padded CIK (e.g., "0000320193"). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses key behavioral traits beyond annotations: parallel fan-out to three external sources, acceptance of ISO dates or relative shorthand, and the return format (structured changes, count, URIs). No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with purpose and examples, but includes extraneous detail (e.g., full list of example queries) that could be trimmed. It is effective but not maximally concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description adequately explains the return format (structured changes, count, citation URIs) and the fan-out behavior. It covers the tool's core functionality, though it could specify pagination or limits.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and descriptions are clear. The description adds minimal value beyond the schema, only clarifying that 'since' accepts relative shorthand like '30d' and that 'value' can be ticker or CIK. Baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose ('What's new with a company in the last N days/months?'), lists example user queries, specifies the three data sources (SEC EDGAR, GDELT, USPTO), and differentiates from sibling tools by focusing on temporal change monitoring rather than general search or entity profiles.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit when-to-use guidance with multiple example queries and explains the tool's behavior (fans out to multiple sources). However, it does not explicitly state when not to use this tool or suggest alternative tools for different contexts.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
rememberAInspect
Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.
| Name | Required | Description | Default |
|---|---|---|---|
| key | Yes | Memory key (e.g., "subject_property", "target_ticker", "user_preference") | |
| value | Yes | Value to store (any text — findings, addresses, preferences, notes) |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are provided (readOnlyHint=false, destructiveHint=false) and the description adds valuable behavioral context: key-value scoping by identifier, persistence differences between authenticated (permanent) and anonymous (24 hours). No contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences, front-loaded with purpose, and every sentence adds value without redundancy or unnecessary detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple store operation without output schema, the description covers purpose, usage, and behavioral details well. It could mention overwrite behavior, but overall it is sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema already describes both parameters. The description adds examples for key naming conventions and clarifies value can be any text, but does not significantly enhance understanding beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: 'Save data the agent will need to reuse later' and specifies cross-session persistence. It distinguishes from siblings 'recall' and 'forget' by mentioning them, making the purpose very clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear guidance on when to use: 'when you discover something worth carrying forward' with concrete examples. It also recommends pairing with 'recall' and 'forget', but does not explicitly state when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
resolve_entityARead-onlyInspect
Look up the canonical/official identifier for a company or drug. Use when a user mentions a name and you need the CIK (for SEC), ticker (for stock data), RxCUI (for FDA), or LEI — the ID systems that other tools require as input. Examples: "Apple" → AAPL / CIK 0000320193, "Ozempic" → RxCUI 1991306 + ingredient + brand. Returns IDs plus pipeworx:// citation URIs. Use this BEFORE calling other tools that need official identifiers. Replaces 2–3 lookup calls.
| Name | Required | Description | Default |
|---|---|---|---|
| type | Yes | Entity type: "company" or "drug". | |
| value | Yes | For company: ticker (AAPL), CIK (0000320193), or name. For drug: brand or generic name (e.g., "ozempic", "metformin"). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, openWorldHint=true, destructiveHint=false, so the description does not need to repeat these. It adds value by explaining the return format: 'Returns IDs plus pipeworx:// citation URIs.' This informs the agent about the output structure and the presence of citation links, which is beyond what annotations provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, with 4-5 sentences. It starts with the primary action and resource, followed by when-to-use, examples, and a note on efficiency. Every sentence adds value without redundancy, making it efficient for an agent to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (2 parameters, 100% schema coverage, no output schema), the description adequately covers all necessary aspects: purpose, usage context, parameter examples, and return format. It provides sufficient information for an agent to select and invoke the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so both parameters have descriptions. The description adds examples ('Apple' → AAPL / CIK 0000320193, 'Ozempic' → RxCUI 1991306 + ingredient + brand'), which clarify how to use the parameters and illustrate expected inputs and outputs, enhancing understanding beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the purpose: 'Look up the canonical/official identifier for a company or drug.' It specifies the verb (look up), resources (company/drug), and provides specifics (CIK, ticker, RxCUI, LEI). It distinguishes the tool by indicating it is a prerequisite for other tools, implying it is the first step before using tools that need official identifiers.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use: 'Use when a user mentions a name and you need the CIK...' and 'Use this BEFORE calling other tools that need official identifiers.' It also notes that it replaces 2–3 lookup calls, indicating it is a consolidated alternative to multiple lookups, providing clear context for when-not-to-use alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
searchBRead-onlyInspect
Search sentences. Use ISO 639-3 codes ("eng","fra","spa","jpn","cmn",…) for from/to.
| Name | Required | Description | Default |
|---|---|---|---|
| to | No | Target translation language (default any). | |
| from | No | Source language (default any). | |
| page | No | 1-based (default 1). | |
| limit | No | 1-100 (default 25). | |
| query | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds ISO 639-3 code format but no further behavioral details like pagination or result limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences with front-loaded purpose and immediate usage note. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Lacks explanation of return format, query purpose, and sorting. With 5 parameters and no output schema, a bit more detail would help, but annotations provide safety context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Adds value by specifying ISO 639-3 codes for language parameters, which the schema lacks. However, the 'query' parameter lacks description in both schema and tool description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Search sentences' with a specific verb and resource. It distinguishes from sibling 'sentence' by implying a broader search, though not explicitly.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives like 'sentence' or 'translations'. The description only provides parameter format advice, not usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
sentenceBRead-onlyInspect
Single sentence by id.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false. The description adds no new behavioral context beyond what annotations provide, which is adequate for a simple fetch operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and front-loaded, with no superfluous words. Each word contributes to the core purpose, making it efficient for the agent to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity of the tool (single parameter, no output schema), the description lacks details about return format or possible failures. While annotations cover safety, the description does not fully equip the agent with expected outcomes.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description does not explain the meaning or constraints of the 'id' parameter beyond implying it identifies a sentence. With 0% schema coverage, the description fails to compensate for the lack of parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Single sentence by id' clearly indicates the tool retrieves a sentence by its identifier, distinguishing it from siblings like 'search' which may return multiple results without a specific ID.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 (e.g., 'search' or 'entity_profile'), leaving the agent to infer the appropriate context from the purpose alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
translationsDRead-onlyInspect
Translations of a sentence.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already set readOnlyHint=true and destructiveHint=false, indicating a safe read operation. The description merely states 'Translations' without adding behavioral details like what is returned or any constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise (4 words) but at the cost of clarity. It lacks useful structure or front-loading of key information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the low schema coverage, no output schema, and relevant siblings, the description is completely inadequate. It does not explain what the tool returns or how it fits into the broader context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, and the description does not explain the single 'id' parameter (e.g., what it identifies). No meaning is added beyond the schema's type and required status.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Translations of a sentence.' is vague; it does not specify the action (e.g., retrieve, list) or what resource is acted upon. Compared to siblings like 'sentence' and 'languages', the purpose remains unclear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 such as 'sentence' or 'languages'. There is no mention of contexts or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
validate_claimARead-onlyInspect
Fact-check, verify, validate, or confirm/refute a natural-language factual claim or statement against authoritative sources. Use when an agent needs to check whether something a user said is true ("Is it true that…?", "Was X really…?", "Verify the claim that…", "Validate this statement…"). v1 supports company-financial claims (revenue, net income, cash position for public US companies) via SEC EDGAR + XBRL. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported), extracted structured form, actual value with pipeworx:// citation, and percent delta. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → numeric comparison).
| Name | Required | Description | Default |
|---|---|---|---|
| claim | Yes | Natural-language factual claim, e.g., "Apple's FY2024 revenue was $400 billion" or "Microsoft made about $100B in profit last year". |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (read-only, open-world), the description details scope (v1, company-financial), data sources (SEC EDGAR + XBRL), return format (verdict, structured form, actual value with citation, percent delta), and efficiency (replaces 4-6 calls). 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, starting with the verb and key action, then usage context, then details. Every sentence adds value, no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description fully explains return values and limitations (v1 scope). It also notes the internal efficiency gain. The tool is simple with one param, so description is complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaning beyond the schema: it explains the type of claims accepted (company-financial) and gives examples, while the schema only describes the 'claim' parameter. Schema coverage is 100% but description enriches context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: fact-checking natural language claims against authoritative sources. It distinguishes from siblings by focusing on verification, and mentions the specific domain of company-financial claims.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description specifies when to use (when an agent needs to check truth of a claim) and provides examples. It does not explicitly list when not to use or mention alternatives, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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