Gemini Crypto
Server Details
Gemini Exchange keyless public market: symbols, ticker, candles, book, trades, price feed.
- Status
- Unhealthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Full call logging
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Tool access control
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Managed credentials
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Usage analytics
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Tool Definition Quality
Average 3.5/5 across 24 of 24 tools scored. Lowest: 1/5.
Many tools have distinct purposes, but there is notable overlap among crypto market data tools (ticker, ticker_v2, price_feed, symbol_details, book, candles, trades) with brief descriptions that may confuse agents. The catch-all ask_pipeworx also blurs boundaries by covering similar queries.
Naming is inconsistent: some tools use verb_noun pattern (ask_pipeworx, bet_research), others are single words (book, candles, trades), and some mix underscores (ticker_v2, price_feed). No uniform convention, making it harder to predict tool names.
24 tools is on the higher end of typical ranges, especially for a server that combines two distinct domains (crypto and general data querying). While not excessive, it feels heavier than a focused crypto server would need.
For a crypto server, it lacks standard operations like wallet, transaction, or swap tools. The inclusion of many non-crypto tools (validate_claim, compare_entities, bet_research) dilutes the domain coverage, leaving gaps in both crypto and general data areas.
Available Tools
24 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 2,522 tools across 575 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?
Annotations provide readOnlyHint and destructiveHint, and the description adds value by detailing that the tool routes to 2,520 tools, fills arguments, and returns structured answers with pipeworx:// citation URIs. No contradictions 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 relatively long but well-structured, starting with a strong directive ('PREFER OVER WEB SEARCH'), then listing domains and examples. Every sentence adds value, though slight trimming could improve conciseness.
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 (2,520 tools, many sources) and only one parameter, the description is comprehensive. It covers usage, examples, behavior (routing, citation), and does not require an output schema to explain return format.
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 single parameter 'question' is minimal in the schema, but the description compensates fully by detailing the types of questions it handles, providing examples, and explaining how the tool processes the question. Schema coverage is 100%, and the description adds significant 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 explicitly states the tool is for factual questions about structured data, listing numerous domains (SEC filings, FDA data, etc.) and providing example queries. It clearly distinguishes it from web search, making the purpose highly specific.
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 instructs to prefer this over web search for factual questions and gives extensive examples of when to use it. However, it does not explicitly state when not to use it, though the context implies it's for queries better answered by structured data.
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 (readOnlyHint=true, openWorldHint=true, destructiveHint=false) are complemented by description detailing classification, fan-out to different packs, and return of evidence packet plus comparison. Adds behavior beyond annotations without 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?
Description is concise (4 sentences) with no superfluous content. Starts with main purpose, then input format, behavior, and usage cases. Well-structured 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?
Given tool complexity, 2 params, and no output schema, description provides adequate context about return (evidence packet + comparison) and fan-out logic. Slightly vague on exact format of evidence packet, but sufficient for agent understanding.
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 descriptions for both parameters. Description adds context on how 'market' can be slug, URL, or question text, and explains 'depth' enum values (quick vs thorough). This exceeds baseline 3.
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 clearly states it researches a Polymarket bet by pulling Pipeworx data, resolves market, classifies bet, fans out to packs, and returns evidence packet with comparison. It distinguishes from siblings like polymarket_edges and validate_claim by being a comprehensive research tool.
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 use cases: 'should I bet on X?', 'what does the data say?', 'is there edge?'. Implies it's superior to discovering packs manually, but lacks explicit 'when not to use' guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
bookDRead-onlyInspect
Orderbook.
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes | ||
| limit_asks | No | ||
| limit_bids | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds no behavioral information beyond what annotations already provide (readOnlyHint, destructiveHint). It does not disclose response format, pagination, or any side effects.
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 word, which is under-specified rather than concise. It lacks necessary structure and front-loads no useful 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 tool has 3 parameters and no output schema, the description is far too minimal. It does not explain return values, errors, or how to use the parameters, leaving the agent with insufficient 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?
With 0% schema description coverage and no parameter info in the description, the agent must guess the meaning of 'symbol', 'limit_asks', and 'limit_bids'. The description adds no semantic value.
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 'Orderbook.' is a noun phrase, not a verb+resource statement. It fails to specify what action the tool performs (e.g., retrieves, fetches). It does not distinguish from siblings like 'candles' or 'ticker'.
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 'candles' or 'trades'. No usage context, prerequisites, or exclusions provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
candlesDRead-onlyInspect
Candles.
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes | ||
| time_frame | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, but the description adds no behavioral context (e.g., data source, pagination, 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely short but under-specified; lacks structure and front-loads no useful information. Conciseness should not sacrifice completeness.
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 and no description of return values. Tool likely returns candlestick data, but that is not stated. 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.
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 parameters 'symbol' or 'time_frame' (e.g., expected format, examples).
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 is 'Candles.' which is a tautology of the tool name. It lacks a verb or explanation of what the tool does (e.g., retrieve, list), making the purpose 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 siblings like 'ticker', 'trades', or 'price_feed'. No context on prerequisites or exclusions.
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?
Annotations already indicate read-only and non-destructive behavior. The description adds specific data sources (SEC EDGAR/XBRL, FAERS, FDA, trials) and return format (paired data + citation URIs), but no further behavioral details like rate limits or permissions.
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 front-loaded purpose, but includes multiple example queries which are helpful but slightly verbose. Could be trimmed 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?
Given no output schema, the description explains return includes paired data and citation URIs. It covers different entity types and data sources adequately for an agent to decide usage.
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 baseline is 3. The description adds significant value by explaining what data each type pulls and providing concrete examples for values, going beyond schema descriptions.
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 'Compare 2–5 companies (or drugs) side by side in one call' with specific verb and resource. It differentiates from siblings by explicitly noting it replaces 8–15 sequential agent calls, implying a multi-entity comparison tool distinct from single-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 usage contexts with example queries like 'compare X and Y', 'X vs Y', etc. It implies efficiency over alternatives but does not state when not to use it, which is minor gap.
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 mark readOnlyHint=true, so safety is clear. The description adds that it returns 'top-N most relevant tools with names+descriptions', but does not disclose ranking logic or pagination. With annotations covering safety, a score of 3 is appropriate.
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 reasonably concise, starting with the core purpose and listing example domains. It embeds usage guidance naturally. A few extra examples could be trimmed, but overall it is well-structured and front-loaded.
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?
With no output schema, the description explains that the tool returns 'top-N most relevant tools with names + descriptions', which is sufficient for a discovery tool. It does not detail ranking or additional metadata, but covers the essentials given the tool's simplicity.
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%, but the description adds value by elaborating on the 'query' parameter with examples ('analyze housing market trends') and stating the 'limit' parameter's default and max. This goes beyond the schema and enhances 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 states 'Find tools by describing the data or task', clearly defining the tool's action. It distinguishes from siblings by advising to use this tool first when many tools are available, contrasting with specific tools like 'trades' or 'candles'.
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 says 'Use when you need to browse, search, look up, or discover what tools exist for...' and instructs 'Call this FIRST when you have many tools available'. This provides clear context and when-to-use guidance.
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 already declare readOnlyHint=true and destructiveHint=false. The description adds value by detailing exactly what data is returned (filings, fundamentals, patents, news, LEI) and mentions citation URIs, providing transparency beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-structured paragraph that front-loads the purpose and usage, then lists outputs. It is appropriately sized for the complexity, though could benefit from slightly more structuring (e.g., bullet points) for 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?
With no output schema, the description adequately lists the return types (filings, fundamentals, patents, news, LEI) and mentions citation URIs. It covers the essential information an agent needs to understand the tool's capabilities.
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 baseline is 3. The description adds meaningful context: it explains the type parameter's limited scope and the value parameter's accepted formats, and directs users to resolve_entity for name inputs, exceeding the schema descriptions.
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 'Get everything about a company in one call' and lists specific data types (SEC filings, fundamentals, patents, news, LEI). It clearly distinguishes the tool's role as a composite aggregator, replacing many individual 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?
The description provides explicit use cases ('tell me about X', 'profile of Acme') and specifies when not to use it (names unsupported, directs to resolve_entity). This gives clear guidance for agent invocation.
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 include destructiveHint=true, and the description adds context about use cases (clearing sensitive data, stale context). It does not contradict annotations and provides additional behavioral clarity.
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 sentences with no wasted words, front-loaded purpose, and efficient structure. Every sentence serves a clear purpose.
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, single-parameter tool with complete schema and annotations, the description fully covers purpose, usage, and implications. No gaps remain.
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 clear parameter description. The description adds no further detail beyond 'by key,' so it meets the baseline but adds minimal value.
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 'Delete a previously stored memory by key,' specifying the verb, resource, and method. It also distinguishes from siblings by pairing with '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?
It explicitly provides when to use: when context is stale, task done, or clearing sensitive data. However, it does not mention when not to use or exclude alternatives, though siblings are named.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
gas_feesCRead-onlyInspect
Gas fee estimate.
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds no extra behavioral context such as return format, volatility, or rate limits, missing an opportunity to clarify the tool's behavior beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise at three words, which is efficient but sacrifices necessary detail. It is front-loaded but insufficiently informative, striking a poor balance between brevity and completeness.
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, no output schema, and existing annotations, the description should provide more context about what the estimate represents, the output structure, and usage notes. It is far from 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 input schema has one parameter 'symbol' with 0% schema documentation. The description does not explain what 'symbol' represents (e.g., token symbol, network ID), failing to compensate for the lack of schema descriptions.
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 'Gas fee estimate.' clearly indicates the tool estimates gas fees, but lacks specificity about which blockchain or context, making it somewhat vague. It distinguishes from siblings like 'price_feed' but could be more precise.
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 like 'price_feed' or 'ticker'. The description does not mention context, prerequisites, or exclusions, leaving the agent without decision support.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
networkDRead-onlyInspect
Network for a token.
| Name | Required | Description | Default |
|---|---|---|---|
| token | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations (readOnlyHint, openWorldHint, destructiveHint) indicate it is a safe read operation. Description adds no further behavioral context, such as what network data 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 a single short sentence, which is concise but fails to be informative. It sacrifices clarity for brevity, leaving the agent uncertain.
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 (1 param, no output schema), the description is drastically incomplete. It does not explain what the tool returns, how to interpret results, or any usage caveats.
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 0% schema description coverage for the 'token' parameter, the description should clarify its format or meaning. 'Network for a token' only hints that token is an identifier, adding no value 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?
Description 'Network for a token' is vague; lacks a verb and does not specify whether it retrieves, sets, or performs some action on the network. It does not differentiate from sibling tools like 'symbol_details' or 'ticker'.
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 'resolve_entity' or 'symbol_details'. No context on prerequisites or exclusions.
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?
Annotations indicate no destructive or read-only hints. The description adds behavioral context: it's a feedback submission tool that affects roadmap, is rate-limited to 5 per day per identifier, and doesn't count against quota. 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 that front-loads the purpose and then provides necessary details. Every sentence is informative and no extraneous content.
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 feedback tool with no output schema, the description covers what the tool does, when to use it, how to format input, and expected usage patterns. It is complete for the given complexity.
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 with descriptions. The description further clarifies the enum values for 'type' and advises on message formatting (specific, 1-2 sentences, 2000 chars). Adds value 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?
The description uses specific verbs like 'Tell' and clearly states the resource 'Pipeworx team'. It enumerates the types of feedback (bug, feature, data_gap, praise) and distinguishes this tool from siblings as the feedback mechanism.
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 the tool (broken, missing, praise) and what not to do (don't paste end-user prompt). It also mentions rate limits and that it's free, guiding correct usage.
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?
Annotations indicate read-only and non-destructive. The description adds details on how the tool searches, groups markets, and checks monotonicity, along with output format (ranked opportunities with reasoning). 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 well-structured with a clear separation of modes, numbered for easy reading. It is slightly lengthy but every sentence adds value, making it effective.
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 describes the return as 'ranked opportunities with suggested trade direction + reasoning'. It covers the main behavior for a two-parameter tool with no required fields.
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 input schema has 100% parameter descriptions. The description adds value by explaining the two modes and providing example inputs (slug, URL, seed question), clarifying parameter usage 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 tool's purpose: finding arbitrage opportunities via monotonicity violations. It distinguishes between two modes (event and topic) and explains what each does, differentiating it from sibling tools like polymarket_edges.
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 tells when to use each mode: event mode for a single event slug, topic mode for cross-event searches. It provides an example of when cross-event is necessary, offering clear guidance on selection.
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 reveals the tool is read-only (consistent with annotations), uses a lognormal model from FRED and live coinpaprika price, and details steps like grouping by asset, fetching price history once, and ranking by edge. This goes beyond annotations and provides full behavioral insight.
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 the main purpose, and efficiently packs details without redundancy. 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?
For a scanning tool with no output schema, the description adequately explains what the tool returns (top N edges, ranked, with trade direction). It covers the necessary context given the tool's complexity.
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?
All three parameters have descriptions in the schema (100% coverage). The description adds minor context like default values but does not significantly enhance understanding beyond what the schema provides.
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 scans high-volume Polymarket markets and identifies those where Pipeworx data disagrees with market price, specifying the resource and action. It distinguishes from siblings by focusing on edge discovery for betting opportunities.
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 frames the tool for the 'what should I bet on today' question, implying its use case for discovering opportunities. It provides context but does not explicitly exclude alternatives or mention 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.
price_feedDRead-onlyInspect
Price feed.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only and non-destructive, but description adds no behavioral context (e.g., data format, update frequency).
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?
Extremely short but under-specified; the single sentence fails to convey meaningful information, sacrificing clarity for brevity.
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?
Even for a simple tool with no parameters and good annotations, the description is incomplete; it doesn't explain what the price feed returns or how it relates to sibling tools.
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, so schema coverage is 100%. Baseline is 4, and description is not misleading; it simply doesn't add parameter information, which is acceptable.
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 'Price feed.' is vague; it doesn't specify what kind of price data (current, historical, stream) or distinguish from sibling tools like 'ticker' or 'candles'.
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; lacks any context about prerequisites or typical use cases.
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 indicate readOnly and non-destructive. Description adds scoping detail ('Scoped to your identifier') and pairing behavior, enhancing transparency beyond structured fields.
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 sentences, front-loaded with primary action, no wasted words. Efficient and clear.
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 simple tool (one optional param, no output schema, annotations provided), description covers purpose, usage, scoping, and pairing thoroughly. No gaps.
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 the key parameter with full description. Description reinforces that omitting key lists all keys, but adds no new information beyond schema. Baseline 3 due to 100% schema coverage.
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 clearly states 'Retrieve a value previously saved via remember, or list all saved keys', specifying verb and resource, and distinguishes from siblings 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?
Provides clear context for use: 'look up context the agent stored earlier', and mentions pairing with remember and forget. Lacks explicit when-not-to-use statements, but adequate.
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?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds behavioral context: it fans out to three sources in parallel, returns structured changes with count and citation URIs. 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 moderately concise, with a single paragraph front-loaded with purpose. Every sentence adds value, though some could be slightly trimmed. It earns its space.
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?
With no output schema, the description covers return type: structured changes, total_changes count, pipeworx:// URIs. It does not mention pagination or error handling, but the context is sufficient given the tool's simplicity.
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 baseline is 3. The description adds value beyond the schema by explaining relative shorthand patterns ('7d', '3m'), acceptable formats, and recommending '30d' or '1m' for typical monitoring.
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: fetching recent changes for a company. It uses a specific verb ('fan out') and resource ('SEC EDGAR, GDELT, USPTO'), and the examples distinguish it from sibling tools like entity_profile or ticker.
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 example user queries and provides context for when to use (e.g., 'what's happening with X?'). It does not explicitly state when not to use or mention alternatives, but the usage context is clear.
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 indicate not read-only and not destructive. Description confirms write behavior and adds persistence details: authenticated users get persistent memory, anonymous sessions retain for 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 four sentences with some compound structures. It is informative without redundancy, though a slight trim could improve conciseness. Front-loaded with the main purpose.
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?
With complete schema coverage and no output schema, the description fully addresses usage, behavior, persistence, and sibling tools. No gaps 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 coverage is 100% with clear parameter descriptions. The description adds value by providing example key naming conventions (e.g., 'subject_property', 'target_ticker') and clarifying that value is free text. This enhances 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 it saves data for later reuse across conversations or sessions, specifying key-value pair storage scoped by identifier. It distinguishes from sibling tools (recall, forget) by mentioning pairing with them.
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?
Explicit guidance on when to use (when discovering something worth carrying forward, like a ticker or preference), with examples. Also notes authentication persistence and session duration, and directs to use recall and forget for retrieval and deletion.
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 it as read-only and non-destructive with open-world hint. The description adds that it returns IDs plus citation URIs, which is useful context. No contradictions; the description complements the annotations well.
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: a few sentences that front-load the purpose, provide examples, and give usage context. Every sentence is informative 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 lacking an output schema, the description adequately explains what is returned (IDs and citation URIs) and provides output examples. Input parameters are fully covered in schema and description. The tool's purpose and usage are thoroughly described.
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% with clear types and meanings. The description adds examples and acceptable input formats (e.g., 'ticker (AAPL), CIK (0000320193), or name'), going beyond the schema to clarify usage.
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 it resolves entity names to canonical IDs for companies and drugs, listing specific ID systems (CIK, ticker, RxCUI, LEI) and giving concrete examples. It distinguishes itself from sibling tools by noting it replaces multiple lookup calls and should be used before tools requiring 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?
Explicitly says when to use: 'Use when a user mentions a name and you need the [IDs] that other tools require as input.' Also instructs to use it BEFORE other tools that need identifiers. It gives the context of replacing 2-3 calls, providing clear guidance on its role in the tool ecosystem.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
symbol_detailsCRead-onlyInspect
Pair metadata.
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate read-only and open-world behavior, but the description adds no behavioral context beyond 'metadata'. It does not clarify what the tool returns or that the symbol may not exist (openWorldHint).
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?
Extremely concise (2 words), but at the cost of informativeness. It is underspecified rather than efficiently 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?
For a simple tool with one parameter and no output schema, the description should at least state what kind of metadata is returned. It fails to provide sufficient context for correct usage.
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 input schema has 0% description coverage and the parameter 'symbol' has no description. The tool description 'Pair metadata' does not add meaning to the parameter.
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 'Pair metadata' vaguely indicates the tool returns metadata about a trading pair, but it does not specify what metadata or distinguish it from sibling tools like 'symbols' or 'ticker'.
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 usage guidelines provided. The description does not mention when to use this tool, when not to, or how it differs from alternatives like 'symbols' or 'ticker'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
symbolsARead-onlyInspect
List trading pairs.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, indicating a safe read operation. The description adds no behavioral context beyond the bare function, such as ordering, completeness, or dynamic nature, which is partially covered by openWorldHint.
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 at three words, with no waste. However, it could be slightly more informative without losing conciseness, e.g., specifying that it returns symbol identifiers.
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 and good annotations, the description is minimally adequate. However, it does not specify the output format (e.g., array of symbol strings), which would help completeness without an output schema.
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, and the schema coverage is 100%. The description adds no parameter information, but the baseline for zero-parameter tools is 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?
Description 'List trading pairs' uses a specific verb ('List') and resource ('trading pairs'), clearly indicating the tool's function. It distinguishes itself from sibling 'symbol_details' which provides detailed information on a single symbol.
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 implies usage for retrieving all trading pairs, but provides no explicit guidance on when to use this tool versus alternatives like 'symbol_details' or other tools. No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
tickerDRead-onlyInspect
v1 ticker.
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate readOnlyHint=true, openWorldHint=true, and destructiveHint=false, but the description adds no further behavioral context (e.g., what data is returned, rate limits). It does not contradict annotations, but provides no added value.
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 short but under-specified, failing to provide meaningful information. It is not appropriately concise; it is simply incomplete.
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 and minimal annotations, the description is insufficient. It does not explain return values, behavior, or how it differs from related tools, making it inadequate for selection and invocation.
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 sole parameter 'symbol' has no description in the schema (0% coverage) and the tool description does not explain what it represents (e.g., a ticker symbol like 'AAPL').
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 'v1 ticker' is a tautology that restates the tool name without explaining what a ticker does. It does not differentiate from sibling tools like ticker_v2, candles, or price_feed.
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 such as ticker_v2 or candles. The description lacks any context for appropriate usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
ticker_v2CRead-onlyInspect
v2 ticker (24h stats).
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes |
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 clear. The description adds '24h stats' which gives context about the data scope, but does not disclose other behaviors such as response format 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 very short (3 words) and front-loaded, but it sacrifices substance for brevity. It lacks necessary detail about what the tool does and its parameters.
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 one parameter, no output schema, and a sibling 'ticker' tool, the description should at least explain how it differs and what the output contains. It is incomplete for effective use.
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 input schema has one parameter 'symbol' with no description and 0% schema coverage. The description does not explain the parameter meaning, expected format, or examples. This is critically insufficient.
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 it is a ticker for 24-hour statistics ('v2 ticker (24h stats)'). The verb is implied (retrieve), and 'v2' distinguishes it from the sibling 'ticker' tool. However, it does not explicitly explain the difference or what data is returned.
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 like 'ticker', 'candles', or 'trades'. The description lacks context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
tradesDRead-onlyInspect
Trade history.
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes | ||
| timestamp | No | ||
| limit_trades | No | ||
| include_breaks | No |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. Description adds no behavioral details beyond what annotations provide. No mention of data freshness, pagination, or rate 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?
Extremely short (2 words), but this is under-specification rather than efficient clarity. The description fails to communicate core purpose or usage.
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?
With 4 parameters, no output schema, and multiple sibling tools, the description is far from complete. Does not mention return format, how parameters affect results, or any constraints.
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 0%; description does not explain any of the 4 parameters (symbol, timestamp, limit_trades, include_breaks). Only the name 'symbol' hints at its meaning.
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 'Trade history' is vague; does not specify verb or resource clearly. Does not distinguish from sibling tools like `candles` or `price_feed` which also deal with historical data.
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 like `candles` or `ticker`. No context about prerequisites or suitable scenarios.
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?
The description extensively discloses behavior beyond annotations: it details the return verdict types, mentions the actual value with citation format (pipeworx://), and explains the efficiency gain ('Replaces 4–6 sequential calls'). This adds significant context that annotations (readOnlyHint, openWorldHint) do not provide, and there is 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 a single, well-structured paragraph of four sentences. It front-loads the core purpose, then covers usage, domain, outputs, and efficiency in logical order. Every sentence adds necessary information without redundancy or 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?
Given the tool has only one parameter and no output schema, the description sufficiently covers purpose, usage, domain, and return structure. It does not explain error cases or the exact citation format, but for a simple fact-checking tool with a clear verdict system, this level of detail is adequate.
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 only one parameter (claim) that is already well-described in the schema (100% coverage), the description adds value by providing concrete examples (e.g., 'Apple's FY2024 revenue was $400 billion') and specifying the supported claim type (company-financial). This enriches understanding beyond the schema's generic description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: fact-check, verify, validate, or confirm/refute a natural-language factual claim. It specifies the domain (company-financial claims for public US companies via SEC EDGAR+XBRL) and provides example queries, making it highly distinct from siblings like ask_pipeworx or compare_entities.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says when to use ('check whether something a user said is true') and gives trigger examples. It also delimits scope ('v1 supports company-financial claims'), providing clear context. However, it does not name alternative tools or specify when not to use, though the domain restriction serves as a soft exclusion.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$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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If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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