lorem
Server Details
Lorem MCP — wraps loripsum.net (free, no auth)
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- pipeworx-io/mcp-lorem
- GitHub Stars
- 0
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Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 3.9/5 across 8 of 8 tools scored. Lowest: 2.9/5.
Most tools have distinct purposes (memory, text generation, entity resolution, meta-querying). However, ask_pipeworx and discover_tools both serve as discovery/query tools, potentially causing confusion. Also, generate_paragraphs and generate_with_options are similar in purpose but differ in output format.
All tool names follow a consistent lowercase underscore pattern (verb_noun or verb_phrase). Even single-word names like forget, recall, remember fit the pattern. No mixing of styles.
With 8 tools, the set is well-scoped for a utility server covering memory, text generation, entity resolution, and meta-tools. Neither too few nor too many.
The memory operations (remember, recall, forget) form a complete CRUD cycle. Text generation covers both plain and formatted output. However, the presence of ask_pipeworx as a catch-all query tool might make some tools redundant, and there are no tools for administrative tasks (e.g., clearing all memories).
Available Tools
9 toolsask_pipeworxAInspect
Ask a question in plain English and get an answer from the best available data source. Pipeworx picks the right tool, fills the arguments, and returns the result. No need to browse tools or learn schemas — just describe what you need. Examples: "What is the US trade deficit with China?", "Look up adverse events for ozempic", "Get Apple's latest 10-K filing".
| 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the tool's behavior: 'Pipeworx picks the right tool, fills the arguments, and returns the result,' which adds value beyond basic purpose. However, it lacks details on limitations, error handling, or performance traits, leaving some gaps in 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?
The description is appropriately sized and front-loaded, starting with the core purpose, followed by mechanism and examples. Every sentence adds value without redundancy, making it efficient and well-structured for quick understanding.
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 (natural language query processing) and no output schema, the description is mostly complete. It explains the input method and provides examples, but lacks details on output format or error cases, which could be improved for full 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?
The input schema has 100% description coverage, so the baseline is 3. The description adds minimal semantic context by framing the parameter as 'Your question or request in natural language' and providing examples, but does not significantly enhance the schema's documentation of the single required 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 clearly states the tool's purpose: 'Ask a question in plain English and get an answer from the best available data source.' It specifies the verb ('ask'), resource ('answer'), and mechanism ('Pipeworx picks the right tool, fills the arguments'). It also distinguishes from siblings by emphasizing natural language input versus other tools that might require structured parameters or browsing.
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: 'No need to browse tools or learn schemas — just describe what you need.' It implies usage for natural language queries versus more technical alternatives, but does not explicitly name when-not-to-use scenarios or list specific sibling alternatives, keeping it at a 4.
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 entities side by side in one call. type="company": revenue, net income, cash, long-term debt from SEC EDGAR. type="drug": adverse-event report count, FDA approval count, active trial count. Returns paired data + pipeworx:// resource 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 carries the full burden. It explains data sources (SEC EDGAR for companies, FDA for drugs), return format (paired data + pipeworx:// URIs), and the efficiency benefit. It does not cover auth requirements or rate limits, but for a read-only comparison tool the key traits are disclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is four sentences, front-loaded with the core purpose, and every sentence adds distinct information (scope, type-specific data, return format, efficiency gain). 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 no output schema, the description provides a high-level summary of return data (paired data + URIs). While it does not detail the exact structure, it is sufficient for an agent to understand what to expect. The tool is not overly complex.
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%, baseline 3. The description adds value by explaining how to specify values for each type (tickers/CIKs for company, drug names for drug) and provides examples. This goes beyond the schema's enum and min/max constraints.
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 compares 2-5 entities side by side in one call, distinguishes between company and drug types, and lists specific data returned (e.g., revenue, adverse-event counts). It also highlights efficiency by replacing 8-15 sequential calls, which differentiates it from siblings like ask_pipeworx or resolve_entity.
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 this tool (comparing entities) and gives examples for each type. It implies it's for batch comparisons but does not explicitly exclude cases where single entity lookup or different comparisons are needed. Sibling tools like ask_pipeworx cover other queries, providing implicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
discover_toolsAInspect
Search the Pipeworx tool catalog by describing what you need. Returns the most relevant tools with names and descriptions. Call this FIRST when you have 500+ tools available and need to find the right ones for your task.
| 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 the full burden. It discloses that the tool returns 'the most relevant tools with names and descriptions,' which gives some behavioral insight into the output format. However, it lacks details on rate limits, error handling, or performance characteristics, leaving gaps in behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose in the first sentence, followed by usage guidance, with no redundant information. Both sentences earn their place by providing essential clarity and instructions, making it highly efficient and well-structured.
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 moderate complexity (search functionality with 2 parameters) and no output schema, the description is mostly complete. It explains the purpose, usage context, and output format, but lacks details on behavioral aspects like limitations or error cases, which could enhance completeness for a search tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents both parameters ('query' and 'limit') with descriptions. The description does not add any parameter-specific details beyond what the schema provides, such as examples or usage nuances, resulting in a baseline score of 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 tool's purpose with a specific verb ('Search') and resource ('Pipeworx tool catalog'), and distinguishes it from siblings by emphasizing its role in discovery when many tools are available. It explicitly mentions returning 'the most relevant tools with names and descriptions,' which clarifies the output.
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 guidance on when to use this tool ('Call this FIRST when you have 500+ tools available and need to find the right ones for your task'), including a specific condition (500+ tools) and alternative context (vs. not using it). It clearly directs the agent to use it for initial discovery in large catalogs.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
forgetCInspect
Delete a stored memory by key.
| 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 provided, the description carries the full burden of behavioral disclosure. It states the tool deletes a memory, implying a destructive mutation, but doesn't clarify if deletion is permanent, requires specific permissions, has side effects, or what happens on success/failure. This leaves significant gaps for a mutation 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 a single, efficient sentence with zero waste. It's front-loaded with the core action ('Delete') and resource, making it easy to parse quickly. Every word earns its place 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?
For a destructive mutation tool with no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits (e.g., permanence, error handling), usage context, and expected outcomes, which are critical for safe and effective tool invocation by an AI agent.
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% description coverage, with the 'key' parameter documented as 'Memory key to delete'. The description adds no additional meaning beyond this, such as key format or examples. With high schema coverage, the baseline score of 3 is appropriate, as the schema does the heavy lifting.
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') and the resource ('a stored memory by key'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'recall' or 'remember', but the verb 'Delete' distinguishes it as a removal operation rather than retrieval or 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 provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an existing memory to delete), exclusions, or how it relates to sibling tools like 'recall' (for retrieval) or 'remember' (for storage).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
generate_paragraphsAInspect
Generate placeholder text paragraphs for mockups and layout testing. Returns plain text without formatting.
| Name | Required | Description | Default |
|---|---|---|---|
| count | Yes | Number of paragraphs to generate (1–10) | |
| length | Yes | Length of each paragraph: short, medium, long, or verylong |
Output Schema
| Name | Required | Description |
|---|---|---|
| text | Yes | Full plain text output |
| count | Yes | Number of paragraphs generated |
| length | Yes | Length of each paragraph |
| paragraphs | Yes | Array of individual paragraphs |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the tool strips HTML tags from responses, which is a useful behavioral trait beyond basic generation. However, it lacks details on rate limits, error handling, or other operational aspects.
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 appropriately sized with two concise sentences that are front-loaded with the core purpose and a key behavioral detail (stripping HTML), with no wasted words or 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 tool's low complexity (simple text generation), no annotations, and no output schema, the description is reasonably complete by specifying the output format and content type. However, it could benefit from more context on when to use versus the sibling tool or handling edge cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents the two parameters (count and length). The description does not add any meaning beyond what the schema provides, such as explaining the semantic impact of length options or count ranges, meeting the baseline for high 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?
The description clearly states the specific action ('generate lorem ipsum placeholder text as plain paragraphs') and resource ('placeholder text'), distinguishing it from the sibling tool 'generate_with_options' by specifying the output format ('plain paragraphs') and content type ('lorem ipsum').
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 generating placeholder text with plain paragraphs, but does not explicitly state when to use this tool versus the sibling 'generate_with_options' or provide any exclusions or alternatives, leaving the context somewhat vague.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
generate_with_optionsCInspect
Generate formatted placeholder text with headers, code blocks, and lists (HTML). Specify which elements to include: headers, code blocks, unordered lists, ordered lists.
| Name | Required | Description | Default |
|---|---|---|---|
| code | No | Include code blocks | |
| count | Yes | Number of paragraphs to generate (1–10) | |
| length | Yes | Length of each paragraph | |
| headers | No | Include random headers (h1–h6) | |
| ordered_lists | No | Include ordered (numbered) lists | |
| unordered_lists | No | Include unordered (bullet) lists |
Output Schema
| Name | Required | Description |
|---|---|---|
| html | Yes | Formatted HTML output |
| count | Yes | Number of paragraphs requested |
| length | Yes | Length of each paragraph |
| options | Yes | |
| plain_text | Yes | Plain text version with HTML tags stripped |
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 states the tool generates HTML with optional elements but lacks behavioral details: no information on output format (e.g., HTML string), error handling, rate limits, or side effects. For a generation tool with no annotation coverage, this is a significant gap in 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?
The description is a single, efficient sentence that front-loads the core purpose ('Generate lorem ipsum HTML') and lists optional features without waste. Every word contributes to understanding the tool's functionality, making it appropriately concise and well-structured.
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 annotations and no output schema, the description is incomplete for a tool with 6 parameters and generation functionality. It doesn't explain what the output looks like (e.g., HTML string), how optional elements are integrated, or any behavioral constraints. For a tool of this complexity, more context is needed to be fully helpful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all 6 parameters. The description adds minimal value beyond the schema by listing optional elements (headers, code blocks, unordered lists, ordered lists), which correspond to some boolean parameters. However, it doesn't explain parameter interactions or provide additional context, meeting the baseline for high 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?
The description clearly states the tool's purpose: 'Generate lorem ipsum HTML' with specific optional elements (headers, code blocks, unordered lists, ordered lists). It distinguishes from the sibling 'generate_paragraphs' by mentioning these HTML formatting options, though it doesn't explicitly contrast them. The verb 'generate' and resource 'lorem ipsum HTML' are 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 provides no guidance on when to use this tool versus alternatives. It mentions optional features but doesn't explain when to choose this over 'generate_paragraphs' or other text generation tools. There are no prerequisites, exclusions, or context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
recallAInspect
Retrieve a previously stored memory by key, or list all stored memories (omit key). Use this to retrieve context you saved earlier in the session or in previous sessions.
| 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?
With no annotations provided, the description carries full burden and does well by explaining key behavioral aspects: it retrieves from both current and previous sessions, supports both single-key retrieval and listing operations, and clarifies the relationship between parameter presence and behavior. It doesn't mention error handling or performance characteristics, keeping it at 4 rather than 5.
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 zero waste. The first sentence states the dual functionality clearly, and the second provides essential usage context. Every word earns its place, and the structure is front-loaded with core functionality.
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 single-parameter tool with no annotations and no output schema, the description provides good coverage of what the tool does, when to use it, and how parameters affect behavior. It doesn't describe return format or error conditions, but given the tool's simplicity, this is reasonably 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 schema has 100% description coverage, so baseline is 3. The description adds meaningful context by explaining the semantic effect of omitting the key parameter ('omit to list all keys') and connecting the parameter to the tool's dual functionality, providing value beyond the schema's technical documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('retrieve', 'list') and resources ('previously stored memory', 'all stored memories'). It distinguishes from siblings like 'remember' (store) and 'forget' (delete) by focusing on retrieval operations.
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 guidance: 'Use this to retrieve context you saved earlier in the session or in previous sessions.' It also explains when to omit the key parameter ('omit key to list all keys'), creating clear conditions for different use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
rememberAInspect
Store a key-value pair in your session memory. Use this to save intermediate findings, user preferences, or context across tool calls. Authenticated users get persistent memory; anonymous sessions last 24 hours.
| 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 the full burden of behavioral disclosure. It effectively describes key behavioral traits: the persistence differences between authenticated users (persistent memory) and anonymous sessions (24-hour lifespan). This adds valuable context beyond the basic storage function, though it doesn't cover potential limitations like storage size 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?
The description is perfectly front-loaded with the core purpose in the first sentence, followed by usage context and behavioral details. Every sentence earns its place by adding distinct value without redundancy, making it appropriately sized and efficiently structured.
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 moderate complexity (storage with persistence rules), no annotations, and no output schema, the description does well by explaining the core function, usage, and key behavioral traits. However, it doesn't specify what happens on success/failure or return values, leaving some gaps that an output schema would typically cover.
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 description coverage is 100%, so the schema already documents both parameters thoroughly. The description doesn't add significant meaning beyond what's in the schema properties, maintaining the baseline score of 3 for adequate but not enhanced parameter semantics.
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 with specific verbs ('store a key-value pair') and resource ('in your session memory'). It distinguishes from siblings like 'recall' (retrieval) and 'forget' (deletion) by focusing on storage, and provides concrete examples of what to store (intermediate findings, user preferences, context).
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 on when to use this tool ('to save intermediate findings, user preferences, or context across tool calls'), which implicitly differentiates it from retrieval or deletion tools. However, it lacks explicit guidance on when NOT to use it or named alternatives for similar storage needs, preventing a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
resolve_entityAInspect
Resolve an entity to canonical IDs across Pipeworx data sources in a single call. Supports type="company" (ticker/CIK/name → SEC EDGAR identity) and type="drug" (brand or generic name → RxCUI + ingredient + brand). Returns IDs and pipeworx:// resource URIs for stable citation. 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 the return values (ticker, CIK, company name, resource URIs) and indicates the operation is a lookup (resolve). It does not mention side effects or authentication, but for a simple 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 concise with three sentences, no fluff. The main purpose is front-loaded, and each sentence contributes useful information 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?
Given the input schema fully describes parameters and no output schema exists, the description sufficiently explains the tool's behavior and return values. It covers what the tool does, what inputs it accepts, and what outputs it produces, making it complete for a simple lookup tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, providing the baseline. The description adds value by explaining the type parameter's v1 support and giving concrete examples for the value parameter (e.g., 'AAPL', '0000320193', 'Apple'), which 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 the tool resolves an entity to canonical IDs across Pipeworx data sources. It specifies the verb 'resolve' and resource 'entity', and distinguishes from siblings like ask_pipeworx and discover_tools by focusing on entity resolution.
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, mentioning it replaces 2–3 lookup calls and accepts ticker, CIK, or name. It does not explicitly state when not to use or list alternatives, but the context is clear for the intended use case.
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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