Excuse
Server Details
excuse MCP — wraps StupidAPIs (requires X-API-Key)
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- pipeworx-io/mcp-excuse
- GitHub Stars
- 0
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Usage analytics
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Tool Definition Quality
Average 4.1/5 across 12 of 12 tools scored. Lowest: 3.2/5.
Most tools have distinct purposes (e.g., excuse_generate for excuses, validate_claim for fact-checking, remember for storage). However, ask_pipeworx and validate_claim both handle factual queries, and entity_profile and recent_changes both provide company info, creating slight overlap.
Naming is inconsistent: some tools use verb_noun pattern (ask_pipeworx, compare_entities, discover_tools), while others are single verbs (forget, recall, remember) or noun phrases (entity_profile, recent_changes). No uniform convention.
12 tools is a reasonable count, but the set covers a broad, unfocused range (data retrieval, memory, feedback, excuses). It feels too many for a single coherent server, yet not excessive in absolute terms.
The server's purpose is unclear. If it's about data retrieval, it lacks data creation/update tools (only feedback for requests). The memory system has save/retrieve/delete, but no tool for listing all memories aside from recall with no key. The excuse generator is isolated, making the set feel incomplete for any single domain.
Available Tools
12 toolsask_pipeworxBRead-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 1,423+ tools across 392+ 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?
No annotations are provided, so the description must fully convey behavioral traits. It states that Pipeworx picks the right tool and fills arguments, implying delegation, but does not disclose important details like latency, rate limits, or whether results are cached. The agent lacks information about side effects or limitations.
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 (4 sentences) and front-loaded with the core action. It includes examples, which aid understanding. However, the second sentence could be more specific about what 'best available data source' means.
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 parameter, no output schema, no nested objects), the description is adequate but lacks details on what the answer format looks like or how errors are handled. The examples help, but more context on the tool's autonomy (e.g., selecting data sources) would improve completeness.
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 single parameter 'question', described as 'Your question or request in natural language'. The description adds examples of expected input (e.g., 'What is the US trade deficit with China?'), which enriches the schema's bare description. Baseline 3 is appropriate since schema already provides basic 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?
The description clearly states the tool's purpose: asking questions in natural language and getting answers from the best data source. It uses a specific verb ('ask') and resource ('question'), and includes examples that illustrate typical use cases. However, it does not explicitly distinguish itself from sibling tools, though its role is distinct as a general question-answering 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?
The description provides examples of when to use the tool (e.g., asking for trade deficits, drug adverse events, SEC filings) but does not explicitly state when not to use it or mention alternatives. The sibling tools like 'discover_tools' and 'remember' suggest there are other query-related tools, but no guidance is given on choosing among them.
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?
No annotations are provided, so the description must disclose behavioral traits. It describes the data returned (e.g., revenue, net income for companies; adverse-event counts for drugs) and resource URIs, but does not mention safety aspects like read-only nature, authentication requirements, or rate limits. This is adequate but not comprehensive.
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, using two sentences to convey purpose, scope, data sources, and benefits. Every word 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?
Given the lack of output schema, the description adequately explains the return data for both entity types and mentions URIs. It is complete for a comparison tool, though it doesn't cover error handling or edge cases. The complexity is moderate and the description meets the need.
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. The description adds value by explaining the meaning of 'type' values and mapping 'values' to specific inputs (tickers/CIKs for company, names for drug) and linking to return fields, going 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: comparing 2–5 entities side by side. It specifies the two entity types (company, drug) with distinct data sources and return fields, and distinguishes itself from sibling tools by emphasizing batch comparison in one call, replacing multiple 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?
The description implies when to use (multiple entities) but does not explicitly state when not to use or provide alternatives. For example, it doesn't mention that for a single entity one might use resolve_entity. The replacement statement is helpful but not a full guideline.
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?
No annotations provided, so description must carry behavioral info. It states the tool returns 'most relevant tools with names and descriptions', implying a search/ranking behavior. Does not mention sorting, pagination, or what happens if query is vague. However, 'returns the most relevant' is sufficient for a discovery tool.
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, each earning its place: first sentence states action and result, second adds usage context ('Call this FIRST'), third provides typical use case. No 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 that output schema is absent and schema coverage is high, description is complete enough for a search tool. It tells what it returns (names and descriptions) and when to use it. Minor missing: no mention of error handling or what happens if no tools match, but not critical.
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 baseline is 3. Description does not add additional semantics beyond schema: schema already explains 'query' and 'limit' well. No extra context needed.
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 verb ('search'), resource ('Pipeworx tool catalog'), and purpose ('find the right tools'). Distinguishes from siblings: explicitly tells agents to call this FIRST when needing to find tools, unlike 'ask_pipeworx' for Q&A or memory tools like 'remember'/'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?
Explicitly states when to use ('Call this FIRST when you have 500+ tools available and need to find the right ones'), implying when not to use (when you already know the tool). No alternative tool named, but the context of 500+ tools makes this a unique entry point.
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?
The description discloses that the tool returns 'pipeworx:// citation URIs' and replaces multiple sequential calls, implying efficiency. However, with no annotations provided, it lacks explicit statements about idempotency or safety traits, though the read-only nature is inferred.
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 compact, front-loading the core purpose, then listing data items, return format, efficiency gain, and an exclusion. Every sentence adds unique value without 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?
The description covers input constraints, output format (citation URIs), and data scope comprehensively. It lacks details about response structure or pagination, but for a single-call profile tool the coverage is sufficient.
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?
Both parameters have full schema descriptions (100% coverage). The tool description adds context by specifying acceptable identifier formats (ticker, CIK) and condition for names, enhancing the schema details meaningfully.
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 provides a 'full profile of an entity across every relevant Pipeworx pack in one call' and lists specific data types (SEC, XBRL, patents, news, LEI). It explicitly distinguishes from sibling tools by noting federal contracts should use usa_recipient_profile.
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 gives explicit when-not-to-use guidance ('For federal contracts call usa_recipient_profile directly') and conditions for input ('Names not supported — use resolve_entity first'). It also implies when to use this tool for comprehensive company profiles.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
excuse_generateARead-onlyInspect
Generate a creative excuse for being late, missing a deadline, or ghosting someone. Returns humorous or plausible excuse text you can use immediately.
| Name | Required | Description | Default |
|---|---|---|---|
| audience | No | ||
| situation | Yes | ||
| excuse_quality | No | ||
| times_used_before | No | How many times you have used this excuse before |
Output Schema
| Name | Required | Description |
|---|---|---|
| excuse | Yes | The generated excuse text |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It states the tool returns humorous or plausible text immediately, but does not disclose whether it is deterministic, idempotent, or has side effects. For a generation tool, this is moderate.
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 concise sentence that front-loads the action and purpose. It is efficient but omits parameter details, earning a high but not perfect score.
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 having an output schema, the description does not elaborate on return values or parameter behavior. With 4 parameters and no annotations, this brief description is insufficient for complete guidance.
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 low (25%), only one property described. The description adds no extra meaning beyond the schema enums (e.g., audience, excuse_quality). It fails to explain optional parameters.
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 it generates a creative excuse for specific scenarios (late, missed deadline, ghosted). It clearly distinguishes from sibling tools (none are excuse generators).
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 when to use (when needing an excuse for these situations) and no alternative sibling exists. However, it does not explicitly state when not to use or provide prerequisites.
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?
With no annotations, the description carries full burden for behavioral disclosure. It states the action ('Delete') but does not mention irreversibility, required permissions, or effects on other data.
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 sentence of six words, containing no fluff. Every word contributes to understanding the tool's function.
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 (1 param, no output schema, no nested objects), the description is mostly complete. It could mention that deletion is permanent or note any preconditions.
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 the only parameter 'key' is clearly described in the schema as 'Memory key to delete'. The description reaffirms its role, adding no extra semantics but not needing to.
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 a specific verb ('Delete') and resource ('stored memory by key'), clearly distinguishing it from sibling tools like 'remember' (store) and 'recall' (retrieve).
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 deletion by key but does not explicitly state when to use this tool over others or mention any prerequisites or caveats.
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?
With no annotations, the description discloses rate limiting and 'free' status, but does not mention whether the tool returns any response or is asynchronous. Additional detail on expected behavior (e.g., fire-and-forget) would enhance transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three concise sentences front-load the purpose, then cover usage constraints and rate limit. No redundant text.
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 feedback tool with no output schema, the description covers purpose, content guidelines, and rate limiting. Could clarify that no response is expected, but is largely sufficient for an AI 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 coverage is 100%; the description adds value beyond schema by advising on message content (describe in terms of tools/data, avoid user prompt) and reinforcing the purpose of the 'type' enum, making parameters more actionable.
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 'Send feedback to the Pipeworx team' and enumerates specific use cases (bug reports, feature requests, missing data, praise), making the purpose explicit and distinct from sibling 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?
Provides clear guidance on when to use (for feedback types) and what to include/exclude (describe in terms of tools/data, no user prompt). Mentions rate limit (5 per identifier per day). Could be improved by explicitly listing when not to use, but the context suffices.
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?
No annotations provided, so description carries full burden. It clearly states the tool is for retrieval (non-destructive) and implies read-only behavior. However, it doesn't specify if listing all keys returns metadata or just keys, but this is a minor gap.
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, no fluff, key information front-loaded. Slightly verbose with the second sentence being somewhat redundant, but overall 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 simple schema and no output schema, description adequately explains what the tool does and when to use it. Could mention that it retrieves from a persistent memory store, but current version is sufficient.
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 description adds minimal extra meaning beyond schema: it clarifies that omitting key lists all. Baseline 3 is appropriate as schema already documents parameter 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?
Description clearly states it retrieves a stored memory by key, or lists all memories if key is omitted. Verb 'retrieve' and resource 'memory' are specific, and it distinguishes 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?
Explicitly says to use key for specific retrieval or omit to list all. Also advises to use this tool to retrieve context saved earlier in the session or previous sessions, providing clear context for use.
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?
With no annotations, the description carries full burden. It discloses parallel fan-out to SEC EDGAR, GDELT, USPTO, acceptable since formats (ISO or relative), and return structure (changes, count, URIs). It doesn't mention rate limits or auth requirements, but for a read tool this is adequate.
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, each serving a purpose: core definition, fan-out details, since format, return structure, and use cases. No redundant information, effectively 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?
Despite lacking an output schema, the description explains the return format (structured changes, count, URIs). It covers behavior for the only supported type and provides enough context for agents to use it 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 descriptions for all 3 parameters. The description adds value by providing examples for since ('30d', '1m') and clarifying acceptable inputs for value (ticker or CIK). This goes beyond the schema's bare 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 'What's new about an entity since a given point in time' and details the behavior for type='company' with parallel fan-out to multiple sources. It distinguishes from siblings like entity_profile (static) and compare_entities (comparison) by focusing on time-based changes.
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 provides use cases ('brief me on what happened with X', change-monitoring workflows) and gives examples of since values. It notes the limitation that only 'company' type is supported, but doesn't cover when not to use it or alternatives beyond implied context.
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?
With no annotations provided, the description carries full burden. It discloses persistence behavior (authenticated vs. anonymous) and storage duration (24 hours for anonymous). This adds valuable context beyond the input schema.
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: first states purpose, second gives usage guidance. No unnecessary words. Front-loaded with the core action.
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 key-value store with 2 required params, full schema coverage, and no output schema needed, the description is complete. It covers purpose, usage, and behavioral differences across auth states.
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 by providing examples for key ('subject_property', 'target_ticker') and value ('findings, addresses, preferences, notes'), which helps the agent choose appropriate values.
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 'store' and resource 'key-value pair in your session memory'. It differentiates from siblings like 'recall' (retrieve) and 'forget' (delete) by specifying the write nature.
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 gives explicit context on when to use: 'save intermediate findings, user preferences, or context across tool calls'. It also distinguishes use cases between authenticated and anonymous sessions. However, it does not explicitly say when not to use or name alternatives.
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?
No annotations are provided, so the description carries the full burden. It discloses that the tool is a read operation (resolving entities), returns specific fields (ticker, CIK, name, URIs), and is a single call. It does not mention side effects, rate limits, or auth, but for a lookup tool, this is adequate.
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. Every sentence provides 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?
The tool has only two parameters with full schema coverage. The description explains the output despite no output schema, and clarifies the single-call efficiency. It could mention error behavior or URI format, but it is sufficiently complete for its 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?
The schema already has 100% coverage with descriptions for both parameters. The description adds value by listing concrete examples (AAPL, 0000320193, Apple) and explaining the type enum structure, which is 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 resolves an entity to canonical IDs, specifies the entity type (company) and input formats (ticker, CIK, name), and explains the output (canonical IDs and URIs). The name 'resolve_entity' matches the purpose, and the description differentiates from siblings by noting it replaces multiple lookup 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?
The description explicitly says 'Replaces 2–3 lookup calls', guiding agents to use this single call instead of multiple. It also restricts usage to 'company' type in v1. However, it does not explicitly state when not to use or compare with sibling tools, but given sibling names, this tool is clearly distinct.
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?
With no annotations provided, the description carries full burden. It discloses the return values (verdict, extracted form, actual value with citation, percent delta) and the supported claim types and data sources. However, it does not mention potential errors, limitations beyond 'v1 supports...', or any side effects, which would be expected for a read-only tool.
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 highly concise with three sentences, each adding value: purpose, scope, output details, and efficiency comparison. No wasted words, and key information is front-loaded. This is an excellent example of conciseness and structure.
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 adequately explains the output format (verdict, extracted form, etc.) and scope. It covers the main use case well, though it omits error handling or edge cases. For this complexity, a score of 4 is reasonable.
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 baseline is 3. The description adds context beyond the schema by specifying the domain (company-financial claims) and providing examples. This is helpful but not extensive, so a score of 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 function: fact-checking natural-language claims against authoritative sources, specifically company-financial claims for public US companies. It defines the verb (fact-check), resource (claims), and scope (financial domain). However, it does not explicitly differentiate from sibling tools like compare_entities, which could be considered a minor gap.
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 when to use the tool (for financial claim verification) and highlights efficiency by replacing sequential agent calls. However, it lacks explicit guidance on when not to use it or alternatives (e.g., compare_entities for entity comparisons), leaving usage context partially implied.
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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