InferMap

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 mentions the action ('Apply') and outcome ('writing the result'), but fails to disclose critical traits such as whether this operation is idempotent, what permissions are required, how errors are handled, or if it modifies the source file. 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 that front-loads the core action and outcome with zero wasted words. Every element ('Apply', 'saved mapping config', 'source CSV', 'renaming columns', 'writing to output file') earns its place by contributing essential context.

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 of a mutation tool with no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits (e.g., side effects, error handling), output format, or usage context, which are crucial for safe and effective tool invocation in this scenario.

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 all three parameters (source, config, output) with clear descriptions. The description adds no additional meaning beyond what the schema provides, such as format details or constraints, 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 ('Apply'), the resource ('saved mapping config'), and the transformation ('renaming columns according to the mapping and writing the result to an output file'). It distinguishes from potential siblings like 'inspect', 'map', or 'validate' by focusing on application rather than creation or validation.

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 like 'map' (which might create mappings) or 'validate' (which might check mappings). It lacks explicit when/when-not instructions or prerequisites, leaving usage context implied at best.

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 of behavioral disclosure. It describes the output content (fields, types, statistics) but doesn't cover critical behavioral traits such as permissions needed, rate limits, error handling, or whether the operation is read-only or has side effects. For an inspection tool with no annotations, this leaves significant gaps in understanding its 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 a single, efficient sentence that front-loads the core action ('inspect a data source') and lists specific outputs without unnecessary words. Every element earns its place by clarifying the tool's scope and deliverables.

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 (data inspection with multiple output metrics), no annotations, and no output schema, the description is moderately complete. It outlines what the tool returns but lacks details on format, limitations, or error cases. It's adequate for basic understanding but has clear gaps for effective agent 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 description coverage is 100%, so the schema already documents both parameters ('source' and 'table') with descriptions. The description doesn't add meaning beyond the schema, such as explaining parameter interactions or constraints, but it implies the 'source' parameter's role in data inspection. Baseline 3 is appropriate when the schema handles parameter 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 ('inspect', 'show') and resources ('data source'), detailing what information it provides (fields, types, sample values, null rates, unique rates, statistics). It distinguishes from siblings like 'apply', 'map', and 'validate' by focusing on data inspection rather than transformation or validation, though it doesn't explicitly name alternatives.

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?

No guidance is provided on when to use this tool versus alternatives like 'apply', 'map', or 'validate'. The description implies usage for data exploration but lacks explicit context, prerequisites, or exclusions, leaving the agent to infer appropriate scenarios.

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 of behavioral disclosure. It adds some context: it describes the method ('weighted scorer pipeline with optimal 1:1 assignment') and output ('Returns mappings with confidence scores and human-readable reasoning'), which goes beyond basic function. However, it lacks details on performance, error handling, or limitations (e.g., data size constraints, runtime).

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 and front-loaded: the first sentence covers the core purpose and method, and the second sentence explains the return value. Both sentences earn their place by adding value. It could be slightly more structured (e.g., bullet points), but it's efficient with zero 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 the tool's complexity (mapping with weighted scoring), no annotations, and no output schema, the description is moderately complete. It explains the method and output format but lacks details on error cases, performance, or integration with siblings. Without annotations or output schema, more behavioral context would be helpful 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?

Schema description coverage is 100%, so the schema already documents all 6 parameters thoroughly. The description doesn't add any parameter-specific semantics beyond what's in the schema (e.g., it doesn't explain how 'domains' affect mapping or clarify 'source'/'target' formats). Baseline 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 tool's purpose: 'Map source columns to target schema using a weighted scorer pipeline with optimal 1:1 assignment.' It specifies the verb (map), resources (source columns, target schema), and method (weighted scorer pipeline). However, it doesn't explicitly differentiate from sibling tools like 'apply', 'inspect', or 'validate', which might have related data processing functions.

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 its siblings (apply, inspect, validate). It mentions the tool's function but offers no context about prerequisites, alternatives, or exclusion criteria. For example, it doesn't clarify if this should be used before or after validation or inspection.

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 of behavioral disclosure. It states the tool 'reports missing source columns and unmapped required fields,' which hints at read-only validation behavior, but doesn't clarify permissions, side effects, error handling, or output format. For a validation tool with zero annotation coverage, this is insufficient.

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 and front-loaded, consisting of two clear sentences that directly explain the tool's function. There's no wasted verbiage, though it could be slightly more structured (e.g., by explicitly mentioning parameters).

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 (validation with file paths and arrays), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what the validation report looks like, error conditions, or how 'required_fields' interacts with the config. This leaves significant gaps for an agent to understand the tool fully.

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 all parameters ('config', 'source', 'required_fields'). The description adds marginal value by implying the relationship between 'source' and 'config' for validation, but doesn't provide additional syntax, format details, or examples beyond what the schema specifies. Baseline 3 is appropriate when 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 tool's purpose: 'Validate that a source file's columns satisfy a saved mapping config.' It specifies the verb (validate) and resource (source file columns against mapping config). However, it doesn't explicitly differentiate from sibling tools like 'apply', 'inspect', or 'map', which prevents a perfect score.

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 its siblings ('apply', 'inspect', 'map'). It mentions what the tool does but offers no context about prerequisites, alternatives, or exclusions. This leaves the agent without clear usage direction.

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