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

With no annotations, the description carries the full burden and does well: it discloses the cost model (one very cheap request per file), that confidence is calibrated, that files are read and returned grouped, and that label descriptions improve accuracy. It does not state that the operation is read-only/non-destructive nor mention rate limits or failure behavior for unreadable files.

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?

Dense and front-loaded: purpose and cost appear in the first sentence, return shape in the second, use cases third, accuracy tip last. Every sentence earns its place, though the parenthetical cost note interrupts the main clause slightly.

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 an eleven-parameter tool with no annotations and no output schema, the description covers purpose, cost, output grouping, and the key accuracy lever. Minor gaps: it only mentions the 'grouped' shape although format supports table/json, and it says nothing about dry_run or include/exclude interplay.

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 eleven parameters are already well documented, including the colon-description convention for labels. The description reinforces that convention but adds no new syntax, defaults, or constraints beyond the schema, so baseline 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?

States a specific verb+resource (classify every file into your labels) plus the mechanism (decision model) and the return shape (files grouped by label). It is clear enough to act on, but it never differentiates itself from the sibling decide_files, which by name sounds closely related.

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?

Concrete when-to-use guidance is given: triage of tests/configs/dead code, sorting documents by topic, flagging files about X. It lacks any exclusions or an explicit pointer to decide_files versus locate_in_files, so the agent must infer the boundary.

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 full burden. It does disclose the key behavioral trait that answers come from a decision model with calibrated probabilities (i.e. LLM-backed, non-deterministic), but it omits practical consequences: one model call per file means cost and latency scaling with path count, answers may be probabilistic rather than ground truth, and no mention of what a failed or truncated file yields.

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?

Three sentences, front-loaded with the core operation, then a concrete example, then a targeted hint about only_matching. Nothing is padded, though the example slightly duplicates the per-field schema descriptions of `options` and `yes`/`no`.

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 an 11-parameter tool with a nested questions array, no annotations and no output schema, the description covers the central concept and question types but leaves gaps: cost/latency implications of per-file model calls, the shape of the returned table (answer columns per question id), and how it relates to classify_files. The 91% schema coverage fills most parameter gaps, keeping this at minimum-viable rather than poor.

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 91%, so baseline is 3, but the description goes beyond it by explaining what each question type means (yes_no flags, one-of choices, ordered scores) and by spelling out only_matching's filtering role with a concrete use. This is valuable because the schema's `type` enum has no description of its own.

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?

States a specific verb and resource (ask typed questions about each file) plus the three question types and that answers carry calibrated probabilities. An agent can picture the operation precisely. However, it never distinguishes this from sibling classify_files, which sounds like an adjacent capability, so the agent has to guess which one to pick.

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?

Usage is only implied through a worked example ('for every file decide uses_db, layer, complexity') and a directive for only_matching. There is no statement of when to prefer this over classify_files, no exclusions, and no prerequisites such as the project root or model availability.

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 and does disclose the read-only nature implicitly plus concrete return content and the default-model flag. It omits auth/rate-limit or freshness details, but for a zero-parameter listing tool the behavioral gap is minor.

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?

Three short sentences, output contents front-loaded, then the downstream usage note, then a sizing hint. Nothing repeats the name or title and no sentence is padding.

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?

No output schema exists, so the description usefully enumerates the return fields and the default indicator. Combined with the cross-tool `model` guidance, an agent has enough to call it and use the result correctly; only pagination/scale behavior is unstated.

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?

Zero parameters gives a baseline of 4. The description goes slightly further by explaining the cross-tool convention for passing a returned id as `model`, which is useful semantics the empty schema cannot express.

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?

States a specific verb+resource ('List the decision models') and enumerates the returned fields (id, context window, input price) plus the default marker, so an agent knows exactly what it gets. It does not name the sibling tools, but their names (classify/locate/decide) make the distinction obvious without it.

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?

'Pass an id as `model` to any other tool' clearly frames this as the discovery step that precedes every other call. It gives a positive usage context but no explicit when-not or prerequisites (e.g. caching, freshness).

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 full burden and does well: it explains the two-pass chunking and line-window scoring, notes that nothing is truncated and that scanning is cheap even for megabytes, and describes the ranked output with probabilities and snippets. It omits permission, rate-limit, and failure-mode details, which keeps it from a 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?

The description is front-loaded with the purpose and then structured into Pass 1, Pass 2, and return-value details. It is dense but mostly earns its length; the two-pass explanation could be tightened slightly.

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 an 11-parameter search tool with no annotations and no output schema, the description provides enough behavioral context: what gets scanned, how scoring works, and what the ranked output contains. It leaves out operational details like API cost semantics or authentication, but the core invocation context is 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 description coverage is 100%, so the schema already documents all 11 parameters in detail. The description reinforces query specificity through the use-case examples but adds no parameter-level guidance beyond what the schema already provides.

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 states a specific verb and resource: 'Find which files and lines most likely contain what you describe.' It adds concrete use cases (bug symptoms, feature implementation, setting definitions) that make the tool's purpose immediately distinguishable from generic file classification or listing.

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 gives clear contexts for when to use the tool, such as locating a bug's cause from symptoms or finding where a setting is defined. It does not explicitly name alternatives like classify_files or decide_files, nor does it state when not to use this tool, so it falls short of a 5.

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