Performance Review

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description mentions that Pipeworx picks the right tool and fills arguments, implying autonomous behavior, but since no annotations are provided, the description carries full burden. It lacks details on limitations, error handling, or data freshness.

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 (3 sentences) with clear examples front-loaded. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simple single-parameter schema and no output schema, the description is adequate but lacks details on return format, confidence, or source attribution.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds that the parameter is a natural language question, which is already clear from the schema description. No additional semantic 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 accepts plain English questions and returns answers from the best data source, distinguishing it from sibling tools like discover_tools or recall which serve different purposes.

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 it (asking natural language questions) and provides examples, but does not explicitly state when not to use it or mention alternatives among siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full behavioral burden. It discloses that the tool returns paired data and pipeworx:// resource URIs, and details the data provided for each type. While it doesn't cover potential errors, rate limits, or authorization needs, it sufficiently describes the key behavioral traits for a read/comparison 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 three sentences, front-loaded with the primary action (compare 2-5 entities side by side). Each sentence provides essential information: the allowed types and their data, the return format (paired data + URIs), and the efficiency gain (replaces sequential calls). No redundant or unnecessary 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?

Given the tool has only 2 parameters and no output schema, the description does a good job explaining the input (type and values) and output (paired data with URIs). However, it lacks details on error handling (e.g., what if an entity is not found or invalid) and edge cases. Overall, it is 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?

The input schema has 100% coverage with descriptions for both parameters (type enum and values array). The description adds value by explaining what data is returned for each type, going beyond the schema's parameter-level descriptions. This helps the agent understand the semantics of the output, which is not documented in an output 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 compares 2-5 entities side by side for two types (company and drug), specifying the data returned (revenue, net income, etc. for companies; adverse-event counts, FDA approvals, etc. for drugs). It also notes it replaces 8-15 sequential agent calls, implicitly distinguishing it from siblings that would do single entity queries.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly says when to use the tool: for comparing 2-5 entities in one call instead of multiple sequential calls. However, it does not provide explicit when-not-to-use conditions or name alternative tools for different scenarios (e.g., single entity query), but the context is clear enough for an agent to infer appropriate use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the burden. It discloses the search behavior (returns most relevant tools with names and descriptions) but does not detail any side effects or limitations beyond what is stated.

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 efficiently conveying purpose and usage. Front-loaded with key action and result. 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 explains what the tool returns ('tools with names and descriptions'), which is sufficient for a search tool. The sibling tools are diverse, so differentiation is clear.

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 covers both parameters with descriptions (100% coverage). The description adds the default and max for limit but not much else beyond schema. 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 a specific verb 'Search' and resource 'Pipeworx tool catalog', clearly stating its purpose. It also differentiates from siblings by positioning itself as a tool discovery mechanism to be called first when many tools are available.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use this tool ('Call this FIRST when you have 500+ tools available') and provides context for its purpose, guiding the agent to use it before other tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, but description covers expected behavior: fetches multiple data sources, returns URIs, and notes a performance caveat for federal contracts. Could mention if it's read-only or any rate limits, but still transparent enough.

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, well-structured sentences. Front-loaded with purpose, then details and caveats. 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?

Good for a profile tool with no output schema; mentions return of citation URIs. Could add more about response structure or pagination, but sufficient given complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and description adds meaning: explains value can be ticker or CIK, names not supported, suggests resolve_entity. Clarifies type is only 'company' for now.

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 returns a full profile for an entity, lists data sources (SEC, XBRL, patents, news, LEI), and replaces multiple calls. It distinguishes itself from siblings like resolve_entity and compare_entities.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides clear when-to-use (comprehensive profile) and when-not (federal contracts, use usa_recipient_profile). Advises to use resolve_entity for names, and notes it replaces 10-15 sequential calls.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It clearly states the tool deletes a memory, which is a destructive action, but does not disclose whether deletion is permanent, requires confirmation, or affects other data. The description is adequate but not rich 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 a single, concise sentence with no wasted words. It is front-loaded with the action and target.

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 required parameter, no nested objects, no output schema), the description is nearly complete. It could mention whether the key must exist or what happens if the key is not found, but overall it covers the essential action for a straightforward deletion 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%: the input schema already describes the 'key' parameter as 'Memory key to delete'. The description adds no additional semantic meaning beyond what the schema provides, so a baseline 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 uses a clear verb ('Delete') and specific resource ('stored memory by key'). It distinguishes from sibling tools like 'recall' and 'remember' which imply retrieval and storage respectively, making its purpose unmistakable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage (when you need to delete a memory) but does not provide explicit guidance on when not to use it or alternatives. Given siblings like 'recall' and 'remember', the description could mention that this tool is for deletion, not retrieval or storage, but it lacks explicit when-not-to-use guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden. It discloses rate limiting ('5 messages per identifier per day') and that it is free. It explains content guidelines (describe in terms of Pipeworx tools/data, avoid verbatim prompts). No contradictions noted.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences: first states purpose, second provides usage guidelines, third mentions rate limit. It is front-loaded and contains no unnecessary words, making it easy to read and understand.

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 without an output schema and with clear input parameters, the description is fully adequate. It covers purpose, usage, constraints, and content rules, leaving no ambiguity for 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% coverage with detailed descriptions for all parameters. The description adds rate-limit and free information but does not enhance understanding of parameter meaning beyond the schema. Baseline of 3 is appropriate given full 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: 'Send feedback to the Pipeworx team.' It lists specific use cases (bug reports, feature requests, missing data, praise) and distinguishes itself from sibling tools like ask_pipeworx (for queries) and discover_tools (for discovery).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description specifies when to use the tool (for feedback types) and provides a negative guideline: 'do not include the end-user's prompt verbatim.' It also mentions rate limits. However, it does not explicitly state when not to use it or suggest alternatives for other purposes, though the sibling context implies appropriate use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. Describes behavior: retrieves by key or lists all. Could add more about data persistence or scope, but sufficient for typical use.

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?

Very concise: two sentences, front-loaded with key action, no wasted words. Effectively conveys 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?

Given low complexity (1 optional param, no output schema), description is complete enough. Explains both retrieval modes and use case.

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 value by explaining the effect of omitting the key. However, it doesn't add extra details beyond 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?

Description clearly states the tool retrieves stored memories by key or lists all memories when key is omitted, distinguishing it from related tools like 'remember' (write) and 'forget' (delete).

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 for retrieving context saved earlier, and mentions omitting key to list all. Does not explicitly mention when not to use, but context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden. It discloses the parallel fan-out, accepted date formats (ISO and relative), and return structure (structured changes, total_changes count, pipeworx:// URIs). It lacks explicit mention of being read-only, but the context makes it clear.

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-loaded with purpose, and uses bullet-like structure for details. Every part is necessary and informative, with 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 explicitly states what is returned: structured changes, total_changes count, and pipeworx:// URIs. It covers inputs, behavior, and output sufficiently for agent decision-making, even for a moderately complex tool with multiple data sources.

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 each parameter has a description, but the description adds valuable context: suggests default relative formats ('30d', '1m') for 'since' and gives examples of ticker and CIK for 'value'. This enhances understanding beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'What's new about an entity since a given point in time' and then details the specific behavior for company type, including parallel fan-out to SEC EDGAR, GDELT, USPTO. It distinguishes itself from sibling tools by focusing on recent changes across multiple sources.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit guidance is given: 'Use for "brief me on what happened with X" or change-monitoring workflows.' It also implies when to use via the supported entity type and date formats. However, it does not explicitly mention when not to use or compare to alternatives like entity_profile.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description effectively covers key behavioral traits: it stores data, session memory, and persistence rules (authenticated vs anonymous). It does not mention any destructive behavior or limitations like key uniqueness or overwrite behavior, but the core transparency is solid.

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, each adding unique value: first defines what the tool does and when to use it, second clarifies persistence rules. No wasted words; highly 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 (2 string params, no output schema, no annotations), the description is complete enough: it explains storage mechanism, use cases, and persistence. It could mention overwrite behavior or key collision, but that is not essential for a basic 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?

The input schema provides 100% coverage with descriptions for both 'key' and 'value'. The description adds value by explaining the purpose of the tool and providing example keys ('subject_property', 'target_ticker'), which helps the agent choose appropriate keys. It does not repeat schema info but augments it.

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', the resource 'key-value pair in your session memory', and the purpose 'save intermediate findings, user preferences, or context across tool calls'. It effectively distinguishes from siblings like 'recall' and 'forget' by specifying the write operation.

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 context on when to use ('save intermediate findings, user preferences, or context') and notes persistence differences for authenticated vs anonymous users. However, it does not explicitly say when not to use or name alternatives like 'recall' or 'forget', though the distinction is implied.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

In the absence of annotations, the description fully discloses the read-only nature (returns IDs and URIs), input constraints (v1 only company), and output fields (ticker, CIK, name, URIs). It omits details like authentication or rate limits but is sufficient for a simple lookup 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?

Two efficient sentences: first defines purpose and scope, second details input/output and benefit. Every word earns its place; no fluff 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?

For a tool with 2 parameters and no output schema, the description covers input format, output format, versioning, and efficiency gain. It is complete and leaves no obvious gaps for an AI agent to understand invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but the description adds substantial value: it explains the v1 versioning, clarifies that 'value' accepts ticker, CIK, or name, provides concrete examples (AAPL, etc.), and describes the return values 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 v1 supports type='company', and lists accepted value formats (ticker, CIK, name). It distinguishes itself by noting it replaces 2-3 lookup calls, making the purpose specific and actionable.

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 context by explaining when to use (to get canonical IDs efficiently) and the benefit (replaces multiple calls). However, it does not explicitly state when not to use or compare against sibling tools like ask_pipeworx or discover_tools, leaving some ambiguity.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.