pulse-mcp

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

With no annotations, the description bears full responsibility for behavioral disclosure. It omits critical details such as whether the tool is read-only, what happens when queries exceed the free cap, error handling, or response format. The lack of these details leaves significant gaps for safe invocation.

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 front-load the core purpose and benefit, and the second sentence succinctly covers free vs. paid access. 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 the complexity (3 parameters, no output schema, no annotations), the description provides essential info on bundling and payment but lacks behavioral completeness, such as what the response contains, error handling, and rate limits. It is adequate for basic understanding but not fully self-contained.

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 67% (two of three parameters have descriptions). The description adds context for the 'queries' parameter by mentioning cross-niche bundling and the 20-query limit, aligning with schema constraints. However, it does not elaborate on individual query types or structure 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 bundles 1-20 cross-niche queries in one call and highlights cost savings, distinguishing it from sibling tools like datafood_query which handles single 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 explains free vs. paid usage with a 5-query limit for free and payment methods, providing clear context for when to use each. It implicitly suggests using datafood_query for single queries but does not explicitly state alternatives.

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?

The description states the tool is 'read-only', a key behavioral trait, which is not inferable from the schema or missing annotations. It also implies a natural language interface. However, it does not disclose other traits like authentication needs, rate limits, or error responses, leaving some gaps. Given no annotations, the description adds moderate value.

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

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

The description is two sentences with no filler words. It front-loads the purpose and then adds a prerequisite. Every sentence is necessary and 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?

The tool has no output schema and is a Q&A interaction. The description explains the input but does not describe the output format or behavior (e.g., how responses are structured, possible errors). For a natural-language tool, the response format is important for correctly interpreting results. The description feels incomplete in this aspect.

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 50% coverage (only 'question' has a description). The description adds meaning by specifying that 'user_id' must be from a 'previously-synced portfolio', which is not present in the schema. This compensates for the missing schema description and helps an agent select appropriate values. The 'question' parameter is aided by the example 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 'Natural-language Q&A on a Plaid-linked portfolio (read-only)', which specifies the verb (Q&A) and the resource (portfolio). It distinguishes itself from sibling tools by the 'read-only' and natural language aspect, though not explicitly naming alternatives. The purpose is clear and 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 a prerequisite: 'Requires user_id of a previously-synced portfolio', which tells the agent when this tool can be used. However, it lacks guidance on when not to use it or what alternatives exist (e.g., datafood_query for non-natural-language queries). The usage context is partially defined.

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 are provided, so the description carries the full burden. It discloses that the tool is free, requires no auth, and returns a 1-row preview. However, it does not explain what happens on error, rate limits, or the format of the response, leaving some behavioral aspects unclear.

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 front-load the key information (purpose, free preview, auth) and then provide an alternative. 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?

For a simple fetch tool with 2 parameters and no output schema, the description covers the essential aspects: what it fetches, cost, auth, and limited output. It could mention the response type or error handling, but for the tool's simplicity, it is sufficiently 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?

Schema coverage is 100%, with both parameters (q and type) having descriptions. The description adds no new parameter-level semantics beyond what the schema already provides, so the baseline score of 3 applies.

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 ('Fetch'), resource ('a single data type from DataFood'), and scope ('Free 1-row preview, no auth required'), effectively distinguishing it from the sibling tool datafood_bundle.

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 guidance on when to use this tool (single queries, free) and when to use the alternative datafood_bundle (for 3+ queries, cheaper). It does not explicitly state exclusions, but the context is sufficient.

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 are provided, so the description carries the full burden. It mentions the returns (session_id and URL) and that it is free, but does not disclose any potential side effects, authorization requirements, or session lifecycle details beyond opening.

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 clear sentence plus an extra note about it being free. It is front-loaded and contains no unnecessary words, though it could benefit from slightly more structure (e.g., listing return values explicitly).

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

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

For a simple tool with two optional parameters, no output schema, and no annotations, the description adequately covers the purpose and return values. However, it does not address session duration, termination, or any rate limits, which are minor gaps.

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

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

Schema coverage is 100%, and both parameters have descriptions in the schema (intent and agent_id are optional). The tool description adds no additional meaning beyond what the schema already provides, so a baseline score of 3 is appropriate.

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

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

The description clearly states the tool 'open a watchable agent session' and specifies the exact return values (session_id and public URL). This distinguishes it from sibling tools like datafood_query which are likely for 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 phrase 'for live observation' implies the context of use, but there is no explicit guidance on when not to use it or comparison with alternatives. However, the description is clear enough for an agent to infer the appropriate scenario.

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