Shakespeare Insult
Server Details
shakespeare-insult MCP — wraps StupidAPIs (requires X-API-Key)
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- pipeworx-io/mcp-shakespeare-insult
- GitHub Stars
- 0
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Usage analytics
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Tool Definition Quality
Average 4.3/5 across 8 of 8 tools scored.
Tools are generally distinct in purpose (query, compare, resolve, memory, feedback), but 'ask_pipeworx' acts as a meta-tool that can cover the same ground as others, potentially causing ambiguity for an agent deciding between direct vs. meta approach.
Naming conventions are mixed: most tools use snake_case with verb_noun structure, but three (forget, recall, remember) are single verbs, breaking consistency. No clear pattern across the set.
8 tools is a reasonable count for the apparent scope of a data query and memory platform. Not overbearing, but the server name 'Shakespeare Insult' suggests a different domain, making the count feel slightly mismatched.
Core operations are present: query, comparison, entity resolution, and persistent memory CRUD (create, read, delete). Missing update for memories, and no tool for managing sessions or user identities, leaving minor gaps.
Available Tools
12 toolsask_pipeworxAInspect
Answer a natural-language question by automatically picking the right data source. Use when a user asks "What is X?", "Look up Y", "Find Z", "Get the latest…", "How much…", and you don't want to figure out which Pipeworx pack/tool to call. Routes across SEC EDGAR, FRED, BLS, FDA, Census, ATTOM, USPTO, weather, news, crypto, stocks, and 300+ other sources. Pipeworx picks the right tool, fills arguments, returns the result. Examples: "What is the US trade deficit with China?", "Adverse events for ozempic", "Apple's latest 10-K", "Current unemployment rate".
| 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?
Discloses that Pipeworx picks the right tool and fills arguments, abstracting away tool browsing. With no annotations, the description fully covers behavioral traits like automated tool selection and result return.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Concise three-sentence description plus examples. Front-loaded with purpose. Could be slightly more structured (e.g., list examples), but no waste.
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 single parameter, no output schema, and no annotations, the description is complete for its simplicity. It explains how the tool works and what to expect. Could add more about limitations, but not necessary for a straightforward Q&A tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema covers 100% with a clear description. The description adds examples and context beyond schema, like 'plain English' and 'no need to browse tools', enhancing meaning for the single 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?
Clearly states it answers plain English questions by selecting the best data source. Specific verb 'ask' and resource 'Pipeworx' with clear examples distinguishing it from sibling tools like discover_tools or shakespeare_insult_generate.
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 describe what you need in natural language, with examples. However, no guidance on when not to use it (e.g., for non-factual questions or specific tool needs), but context signals and sibling names imply it's the general Q&A tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
compare_entitiesAInspect
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 provided. The description adds value by detailing returned data per entity type and mentioning pipeworx:// URIs, but omits auth needs, rate limits, or error behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences densely packed with purpose, usage, and output info, front-loaded with key action. No filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema, but description specifies exact metrics per entity type (revenue, net income, etc.). Lacks output format details but adequate for typical 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?
Schema coverage is 100%; description does not add parameter-specific meaning beyond schema. It contextualizes values (tickers/CIKs vs drug names) but not enough to raise baseline from 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?
The description clearly states the verb 'compare' and the resource '2–5 entities' with specifics for company and drug types. It distinguishes itself by noting it replaces 8–15 sequential agent calls, making its purpose and advantage explicit.
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 efficient multi-entity comparison, contrasting with sequential calls. However, it does not explicitly state when not to use it or compare with siblings like resolve_entity, leaving some guidance implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
discover_toolsAInspect
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 are provided, so the description carries full burden. It states the tool 'returns the most relevant tools with names and descriptions' and mentions default limit and max limit. However, it doesn't disclose whether the tool is read-only, any side effects, rate limits, or how relevance is determined. For a search tool, the description is adequate but could be more explicit about non-destructive behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long, front-loads the purpose, and each sentence serves a clear function: first explains what it does, second gives usage guidance. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that there is no output schema, the description should mention what the return value contains. It does: 'Returns the most relevant tools with names and descriptions.' This is sufficient. The tool is simple (2 params, no enums, no nested objects), so the description covers the essentials. Could mention that it returns a list, 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 coverage is 100% (both parameters have descriptions). The description adds value by explaining the query parameter format with examples (e.g., 'analyze housing market trends') and clarifying that limit has a default (20) and max (50). However, the schema already provides parameter names and descriptions, so the description's additional context is moderate.
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: 'Search the Pipeworx tool catalog by describing what you need.' It specifies the verb 'search', the resource 'tool catalog', and the action 'returns the most relevant tools with names and descriptions.' This distinguishes it from siblings like 'ask_pipeworx' (which likely answers questions) and other memory 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 explicitly advises: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This provides clear when-to-use guidance, especially given the large number of tools (500+). It also implies not to call this when you already know the tool, or when fewer tools are available.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
entity_profileAInspect
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 the bundled data sources and the output format (pipeworx:// citation URIs). It notes that federal contracts are excluded due to speed concerns. However, it does not explicitly mention read-only nature, error handling, or rate limits, though the lack of annotations raises the bar slightly.
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 (5 sentences), front-loads the overall purpose, and provides detailed breakdown of data sources and usage notes without extraneous 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's complexity (bundling multiple data sources) and simple schema, the description covers all needed information: what data is included, how to identify entities, output format, and when to use alternatives. No output schema exists, but the description suffices.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema coverage, the baseline is 3. The description adds value by explaining the `value` parameter accepts ticker or CIK and notes that names are not supported, which is beyond the schema description. It also interprets the `type` enum.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool retrieves a full entity profile across multiple Pipeworx packs, listing specific data sources (SEC filings, XBRL, patents, news, LEI). It distinguishes from siblings by noting it replaces multiple sequential calls and explicitly directs to another tool for federal contracts.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit when-to-use and when-not-to-use guidance: it replaces 10-15 sequential calls, and for federal contracts, it directs to use `usa_recipient_profile` directly. It also hints at using `resolve_entity` if only a name is available, clarifying prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
forgetAInspect
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?
No annotations are provided, so the description carries the full burden. It correctly states the delete operation but does not disclose behavioral traits like whether deletion is permanent, if confirmation is required, or effects on other memories. Basic transparency, but not thorough.
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, front-loading the action and object. No wasted words; every part is essential.
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 a simple tool with 1 required parameter, no output schema, and no nested objects, the description is nearly complete. It lacks only behavioral nuance (e.g., irreversibility) which would make it perfect.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 100% coverage and already describes the key parameter. The description adds the context that the key identifies the memory to delete, which aligns with the schema. No additional meaning beyond the schema is necessary, so a score of 4 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 action ('Delete'), the resource ('stored memory'), and the identifier ('by key'). It is specific and distinguishes itself from siblings like 'recall' and 'remember' which handle retrieval and storage.
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 this tool (when you need to delete a memory by key) but provides no guidance on when not to use it or alternatives. Since there are related tools for memory operations, explicit guidance would be helpful.
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?
No annotations are provided, so the description bears the full burden. It discloses the rate limit and the constraint about excluding user prompts. No contradictory or hidden behaviors are implied.
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: two sentences that clearly convey purpose, usage, and constraints without any filler. Perfectly 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?
Given the tool's simplicity (send feedback with no output), the description covers all necessary aspects: purpose, when to use, what to avoid, and rate limits. The optional context parameter is sufficiently explained in the 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?
Schema description coverage is 100%, so parameters are already well-documented in the schema. The description adds value by reinforcing specificity in messages and the rate limit context, which is not in 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 'Send feedback to the Pipeworx team' and enumerates specific use cases (bug reports, feature requests, missing data, praise). This distinguishes it from sibling tools like ask_pipeworx (querying) or compare_entities (comparison).
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 tells when to use (for feedback types) and what not to include (end-user's prompt verbatim). Also mentions a rate limit of 5 messages per identifier per day, providing clear operational guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
recallAInspect
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 are provided, so the description carries full burden. It discloses that the tool retrieves or lists memories, but does not mention any behavioral traits like persistence across sessions, limitations on key format, or what happens if key is missing. Given no annotations, the description is adequate but not rich.
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, clear and front-loaded. No extraneous words. Slightly wordy with 'previously stored' and 'earlier in the session or in previous sessions', but acceptable.
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 a simple tool with 1 optional parameter and no output schema, the description covers the core functionality. It explains both retrieval modes (by key vs list) and the context of use (previously stored memories). No major gaps for this complexity level.
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 meaning by explaining the effect of omitting the 'key' parameter (list all keys) and the purpose ('retrieve context you saved earlier'). This goes beyond the schema's simple 'omit to list all keys'.
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 'retrieve' and the resource 'stored memory', and distinguishes between retrieving by key vs listing all. This differentiates it from siblings like 'remember' and 'forget'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains when to use the tool ('to retrieve context you saved earlier') and implicitly distinguishes from 'remember' (store) and 'forget' (delete). However, it does not explicitly mention when not to use it or provide alternative tools for other scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
recent_changesAInspect
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?
No annotations are provided, so the description carries full burden. It discloses parallel fan-out to multiple sources, acceptable input formats (ISO date or relative), and output components (structured changes, total_changes, URIs). No destructive actions or rate limits are mentioned, but for a read-only public data tool, the disclosure 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 concise: three sentences with no wasted words. The first sentence states the purpose, followed by details on entity type and data sources, then input format and output structure. Information is front-loaded and well-organized.
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 (multiple data sources, parallel processing, flexible input formats), the description covers essential aspects: what sources are queried, input format guidelines, and output structure. It does not detail each source's specific output fields, but that is likely acceptable for an AI agent. No output schema is provided, but the description outlines key return values.
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 descriptions for all three parameters. The description adds value by reinforcing acceptable formats for 'since' (e.g., ISO date or relative like '7d') and providing examples for 'value' (ticker or CIK). It also adds implicit guidance on typical usage (e.g., '30d' for 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: 'What's new about an entity since a given point in time.' It specifies supported entity type (company), data sources (SEC EDGAR, GDELT, USPTO), and output structure. This distinguishes it from sibling tools like entity_profile (full profile) and compare_entities (comparison).
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 recommends using the tool for 'brief me on what happened with X' or change-monitoring workflows. While it does not explicitly list exclusions or alternative tools, the context of sibling tools makes usage clear. The guidance is strong but lacks explicit when-not-to-use instructions.
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?
No annotations are provided, so the description carries full burden. It discloses important behavioral traits: persistence varies by authentication status (authenticated users get persistent memory; anonymous sessions last 24 hours). However, it doesn't mention overwrite behavior (same key overwrites?), storage limits, or data privacy implications.
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-loaded with purpose, then usage guidance, then behavioral note. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple input schema (2 string params, no output schema, no nested objects), the description covers the essential behavioral aspects. It could mention that values are plain text (already implied) or maximum size limits, but overall it's sufficient for a straightforward key-value store.
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% and provides clear parameter meanings (key examples, value as any text). The description adds general context but no additional parameter-specific details beyond the schema, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses specific verb+resource ('Store a key-value pair in your session memory') and clearly distinguishes from siblings like 'recall' (retrieve) and 'forget' (delete), making its purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool ('save intermediate findings, user preferences, or context across tool calls') and differentiates persistence based on authentication. It does not explicitly mention when not to use it or compare to alternatives, but the sibling distinction is implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
resolve_entityAInspect
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 full burden. It discloses that it accepts certain inputs and returns canonical IDs and URIs, implying a read-only operation. However, it doesn't discuss rate limits, authentication, or error scenarios.
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 with no redundancy. Each sentence serves a purpose: stating overall function, providing details, and highlighting benefit. Very concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description adequately explains return values (ticker, CIK, name, URIs). It also notes the tool replaces 2-3 calls, which is helpful context. The two required parameters are clearly explained both in schema and description.
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 for both parameters. The description adds value by providing concrete examples (AAPL, CIK) and clarifying the v1 constraint ('type=company'), making parameter semantics clearer.
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, specifying verb and resource, and distinguishes from sibling tools which are unrelated (ask_pipeworx, discover_tools, etc.).
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 explains when to use (to resolve a company entity with ticker, CIK, or name) and that it replaces multiple lookup calls, but does not explicitly state when not to use or mention alternative tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
shakespeare_insult_generateAInspect
Generate a Shakespearean insult. Classical mode (no target) uses authentic vocabulary. Targeted mode uses Haiku for bespoke devastation.
| Name | Required | Description | Default |
|---|---|---|---|
| target | No | Target for a bespoke insult. Omit for classical random. | |
| severity | No | ||
| recipient | No | ||
| translate | No | Include modern English translation |
Output Schema
| Name | Required | Description |
|---|---|---|
| insult | Yes | The generated Shakespearean insult |
| translation | No | Modern English translation of the insult |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It discloses the mode-dependent vocabulary style (authentic vs. Haiku) but does not mention side effects, rate limits, or output format constraints beyond the 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?
The description is two sentences long, with no redundant text. The first sentence states the core purpose, and the second explains the two modes efficiently.
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 output schema exists (handling return values), and the tool has 4 optional parameters, the description adequately covers the mode switching. However, it does not explain the severity scale or recipient options, which are left to the 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?
Schema coverage is 50% (only 'target' and 'translate' have descriptions). The description adds context about omitting 'target' for classical mode, but does not explain 'severity' or 'recipient' even though they have enums. This adds marginal 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?
The description clearly states the tool generates a Shakespearean insult and explicitly distinguishes two modes (classical and targeted), providing a specific verb-resource pairing.
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 clear context for when to use each mode ('no target' for classical, 'targeted' for bespoke), but lacks explicit when-not-to-use guidance or comparison with sibling tools, which are in a different domain.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
validate_claimAInspect
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, the description carries the full burden. It discloses the data sources (SEC EDGAR + XBRL), the return verdict structure, and the efficiency benefit. It lacks details on potential errors or prerequisites but is generally transparent about the tool's behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no wasted words. The first sentence clearly states the main purpose, and the second sentence adds crucial scope and return details. It is front-loaded and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has a single parameter, no output schema, and no annotations, the description provides sufficient context about input format, supported domains, and output structure. It could mention error handling but otherwise covers the essential information for an AI agent to use 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?
The schema coverage is 100% for the single parameter 'claim'. The description adds meaningful examples (e.g., 'Apple's FY2024 revenue was $400 billion') and specifies the supported metric types, providing context beyond the schema's minimal 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 it fact-checks natural-language claims against authoritative sources, specifically company-financial claims via SEC EDGAR + XBRL. It lists return values and distinguishes itself from sibling tools like ask_pipeworx or compare_entities by specializing in factual verification.
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 specifies the supported claim types (company-financial for US public companies) and notes it replaces multiple agent calls. It implies when to use this tool, though it does not explicitly state when not to use it or name alternative tools for unsupported claims.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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{
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