datafood-mcp

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 key behaviors: bundling limit (1-20), free preview cap (5), and payment requirements via Stripe session_id or x402 header. This adds significant behavioral context beyond the schema, though it omits error handling and result structure.

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

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

The description is extremely concise, using two sentences to convey purpose, quantity limits, cost savings, and payment methods. Every phrase earns its place with no fluff or repetition.

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 3 params and no output schema, the description covers the core usage: how to bundle, payment, and limits. It does not describe the return format or error behavior, but given the complexity and the presence of the schema for limits, it is fairly complete. Slight gap for 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 coverage is 67% (free and session_id have descriptions). The description adds meaning to 'free' (preview up to 5) and 'session_id' (paid via Stripe), but does not clarify the 'queries' parameter's 'q' field or the structure of each query object, relying on the schema's type enum. Moderate 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 it bundles 1-20 cross-niche queries into one call, using the specific verb 'Bundle' and clearly distinguishing from per-API calls (likely the sibling datafood_query). This provides a strong sense of the tool's role and scope.

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?

It implies use for batching multiple queries to achieve cost savings, and explains free vs paid modes with payment methods. However, it does not explicitly state when not to use it (e.g., for a single query) or name alternative tools, but the 'vs. per-API' phrase gives clear context.

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 discloses that the tool is read-only and requires a previously-synced portfolio user_id, which are useful behavioral traits. However, with no annotations, it does not cover error behavior, data freshness, or invalid user_id handling, limiting 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 extremely concise: two sentences, front-loaded with the core purpose and followed by the key prerequisite. No redundant 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?

For a simple read-only Q&A tool, the description covers purpose, safety, and a parameter prerequisite. However, it does not mention the return format or provide guidance on choosing this tool over siblings, leaving gaps for agent selection.

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 description adds context for user_id by specifying it must belong to a previously-synced portfolio, partially compensating for the schema lacking a description for that parameter. The question parameter already has an example in the schema, and the description does not add further meaning.

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

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

The description clearly states the tool performs natural-language Q&A on a Plaid-linked portfolio and is read-only. It identifies the specific resource and action but does not explicitly differentiate it from sibling tools like datafood_query or 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 a prerequisite (user_id of a previously-synced portfolio) but no guidance on when to use this tool versus siblings. There is no when-not-to-use or alternative recommendation.

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 effectively discloses key behaviors: it returns a free 1-row preview and requires no authentication. This adds useful context about usage limits and access requirements, though it does not detail response structure or potential 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 two short, front-loaded sentences. Every sentence adds value: the first states the core action, the second explains cost and alternative usage. No redundant or filler content.

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

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

The description adequately covers purpose, usage alternative, and important constraints (free preview, no auth) for a simple fetch tool with only two parameters. It lacks details on return format, but since there is no output schema, a brief explanation of the preview would improve completeness; still, it is sufficient for a basic 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 input schema provides 100% description coverage for both parameters, including an enum for 'type'. The description adds no further parameter-specific details beyond what the schema already contains, so it meets the baseline but does not exceed 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 tool fetches a single data type from DataFood, using a specific verb and resource. It also distinguishes itself from the sibling datafood_bundle by noting that bundle is for 3+ queries, making the purpose immediately clear.

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 versus datafood_bundle, mentioning cost and query volume. However, it does not address the other sibling tools (portfolio_ask, watch_session), so the guidance is not fully comprehensive.

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 burden. It does disclose key behavior: returns session_id and a public URL, and notes it is 'Free'. However, it omits details about session lifecycle, prerequisites, or potential side effects (e.g., whether the agent starts executing). This is basic but not rich 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, front-loaded sentence with no wasted words. It states the action, outputs, and a notable feature (Free) efficiently, earning a perfect score for conciseness and structure.

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

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

For a simple tool with no required parameters and no output schema, the description is fairly complete: it covers purpose, return values, and cost. It lacks usage exclusions and session lifecycle details, but given the low complexity, this is acceptable. A small gap in alternative guidance prevents a 5.

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% since both parameters have descriptions. The tool description itself does not add extra meaning beyond the schema, but the schema already documents each parameter adequately. Baseline 3 is appropriate as the description adds no value over the structured field descriptions.

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

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

The description clearly identifies the tool's purpose with a specific verb+resource: 'Open a watchable agent session'. It also distinctively notes the tool returns a session_id and a public /watch/{id} URL, setting it apart from siblings like datafood_query or datafood_bundle. The purpose is unambiguous and no tautology is present.

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 indicates the tool is for 'live observation' of an agent session, implying when to use it. However, it does not explicitly compare with alternatives or state when not to use it. Given the sibling tools, this is a clear but not fully explicit usage context.

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