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

With no annotations provided, the description carries the full burden, and it does so well. It discloses that the tool runs no vision model, returns images unchanged, redacts evidence, and focuses on failed/slow requests. This prevents false expectations about image analysis.

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 compact and front-loaded. The first sentence states the core function, and the second paragraph adds a necessary and behaviorally important caveat. Every sentence earns its place without filler.

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 covers the core behavior and a major misuse case clearly. The main gap is that it does not explain how an agent should supply HAR data via har_json versus har_path, or what the context parameter is for.

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 0%, and the description partially compensates: it explains image_base64's echo-only behavior and implies slow_threshold_ms relates to slow requests. However, the roles of context, har_json, and har_path are left implicit, relying on parameter names alone.

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 action and resource: extract structured evidence from a HAR / browser network-tab export, listing failed and slow requests, correlation headers, and redaction. It also corrects the misleading 'visual' in the tool name by explicitly saying it runs no vision model.

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 gives explicit when-to-use guidance: use it to extract HAR-based evidence, and explicitly warns against passing a screenshot the model already has. It also explains the only case where image_base64 should be passed, which is clear usage 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?

No annotations are provided, so the description carries the full burden for behavioral disclosure. It implies the tool mutates no state ('extract and rank' is read-only) but doesn't explicitly state it's non-destructive. More importantly, it doesn't disclose what happens if evidence_snippets contains no shared IDs, how ranking works, or whether it has side effects. For a tool with no annotation coverage, this is a notable gap.

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 with zero waste. The core action is stated first, followed by a concrete use case. Every word earns its place, and it's appropriately sized for a simple tool.

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 simplicity (one required param, no output schema, no annotations), the description is mostly complete for calling the tool. It covers what it does and when to use it. However, it lacks detail on the output format (what 'rank' means) and edge cases (e.g., empty input), which would be useful but not critical. It's adequate but could be enhanced.

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 0%, but there's only one parameter, evidence_snippets, and the description clarifies it means log snippets already collected elsewhere. This adds meaning beyond the mere array-of-strings type. However, the description doesn't clarify expected format (e.g., raw log lines vs. structured JSON), which would help. Baseline 3 is appropriate given the single parameter and minimal description 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 action (extract and rank correlation IDs) and the resource (correlation IDs across log snippets), and it gives a concrete example use case (sync-to-async hop). This distinguishes it from the listed siblings, which are investigation planning, visual evidence analysis, querying sources, coverage, and RCA validation. However, it does not explicitly name a sibling to contrast with, and the verb 'correlate' is slightly less specific than 'extract and rank.'

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 context on when to use it: when you have log snippets already collected and need to carry an identifier forward across an async hop. It implies it's for analysis after evidence collection, differentiating from query_generic_source and list_generic_sources which are for gathering sources. However, it doesn't explicitly say when NOT to use it or mention 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?

With no annotations, the description carries the full behavioral burden. It discloses a non-obvious behavior: an empty query result does not prove absence when unknown_coverage is true. This is a valuable caveat beyond the schema. It does not mention return format or side effects, but the core behavioral risk is addressed.

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 focused sentences: the first states what the tool does and its data source; the second adds a crucial edge-case warning. Every sentence earns its place, and the most important caveat is clearly highlighted.

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 is adequate for a one-parameter lookup, and the unknown_coverage caveat is valuable. However, there is no output schema, and the description does not explain the shape of the response, how unknown_coverage is represented in the result, or how this tool relates to the sibling query/list tools. This leaves some burden on the agent to infer behavior.

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 provides only a bare 'resource_type' string with no description. The tool description clarifies that the value refers to a resource type known to topology.yaml Icaria, which adds some context. However, it does not give allowed values, examples, or details on how the resource_type must be formatted, so compensation for the 0% schema coverage is only partial.

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 names a specific operation ('look up which observability surfaces cover') and a concrete data source ('from topology.yaml'). It also distinguishes itself from siblings like query_generic_source and list_generic_sources by focusing on coverage rather than generic querying 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?

The intended use is clear: call this tool when you need coverage information for a resource type. It even includes an important interpretive rule about empty results when unknown_coverage is true. It stops short of explicitly naming sibling alternatives or stating when not to use it, but the context is strong.

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 must disclose behavioral traits. It only says 'list' without specifying read-only nature, return format, or side effects. The existence of an output schema partially mitigates this, but the description itself adds no behavioral context beyond the verb.

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 that states the purpose with no filler. It is concise and structured exactly as needed for a zero-parameter tool.

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 listing tool with no parameters and an output schema, the description is adequate. It does not mention limitations or clarify the exact meaning of 'sources,' but given the low complexity, this is acceptable. The output schema likely covers return values.

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 tool has zero parameters, and the schema coverage is 100%. According to the rubric, a baseline of 4 is appropriate since there is no parameter information needed. The description does not need to add parameter semantics because there are none.

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 'list' and the resource 'sources declared in adapters.yaml', making the purpose unambiguous. It doesn't explicitly contrast with sibling tools, but the verb 'list' vs 'query' suggests a read-only enumeration, which is sufficient for purpose clarity.

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 query_generic_source. The description merely states what it does without any context on selection criteria or exclusions, leaving the agent to infer usage from the name.

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 reveals meaningful behavior: classification, vendor spellings, source mapping, and time-window widening for async ids. It stops short of explicitly stating whether any underlying source queries are executed or whether the operation is strictly read-only, leaving a small but not critical gap.

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 tight and front-loaded. The first words 'START HERE' immediately signal the primary use case, the middle sentence enumerates the tool's outputs efficiently, and the final sentence names the alternative. No filler or tautological phrasing exists.

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?

There is no output schema and no annotations, yet the description covers the main triggering scenario, the outputs, and the key sibling. The only notable gaps are the undocumented environment parameter and the lack of an explicit statement that the tool does not perform direct source queries, preventing a perfect score.

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 0%, so the description must compensate. It richly explains the required identifier parameter with examples like 'session id, request id, trace id, or correlation id.' However, the optional environment parameter is never mentioned, so its role and accepted values remain unclear.

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 the tool does: it classifies the identifier's shape, returns vendor spellings, names answering sources and coverage surfaces, and suggests a time window. It also distinguishes itself from the sibling correlate_ids, so an agent can tell them apart without opening schemas.

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 opens with an explicit trigger condition: 'START HERE when the user hands you a bare identifier... with no logs yet.' It also gives a clear alternative path: 'Use correlate_ids instead once you already have log snippets in hand,' which is direct guidance for choosing 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?

No annotations exist, so the description carries the full behavioral burden. It reveals the tool is a config-templated REST query, but does not state whether it is read-only, how pagination/cursor works, what the response looks like, or what failures/errors may occur. Too much behavior is left undisclosed for a tool with zero annotation support.

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 concise sentences, front-loaded with the core purpose and immediately followed by the critical usage constraint. Every sentence earns its place with no redundant filler.

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 covers purpose and tool selection but leaves essential invocation context missing: parameter semantics, cursor/pagination behavior, expected request shape, and return format. With no annotations and no output schema, this is insufficient for an agent to call the tool confidently on its first attempt.

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 0% and the description provides no explanation of source, params, start, end, or cursor. The params object is open-ended (additionalProperties: true), so an agent has no idea what keys or value formats to supply. The description does nothing to compensate for the schema's silence.

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 action ('Query') and a specific resource ('a source declared in adapters.yaml'), plus the mechanism ('config-templated REST call'). This clearly distinguishes the tool from siblings like list_generic_sources and makes its scope unambiguous.

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 an explicit when-to-use rule: 'Use ONLY when no vendor MCP (Datadog/Splunk/Loki/etc.) already covers this source' and tells the agent to prefer connected MCPs first. This is direct, actionable routing guidance with no ambiguity.

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 behavioral burden. It discloses that validation is deterministic, that the result should be emitted inside a specific envelope, and that a Stop hook re-runs the check. This is valuable context beyond what the schema provides, though it does not describe failure behavior or side effects.

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 compact and front-loaded: the primary purpose comes first, followed by the crucial calling convention. Every sentence earns its place, and there is no redundant restatement of the tool name or schema.

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 covers when to call the tool and how to format the output envelope, which is useful. However, with no output schema and no parameter explanations, the agent still lacks enough detail to know what a valid 'structured claim' looks like or how to populate investigation_log correctly.

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 0%, and the description never explicitly explains claim_json or investigation_log. The phrase 'structured claim' hints at claim_json's purpose, but the agent is left without guidance on the expected claim structure, required fields, or how investigation_log affects validation.

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 action and resource: 'deterministically lint a draft RCA's structured claim before you post it.' This clearly differentiates validate_rca from the sibling tools, which handle correlation, investigation planning, source queries, and visual evidence — none of which are about validating a draft claim.

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 gives explicit usage context: 'Call this before finalizing any RCA.' It also explains the special Stop-hook behavior on Claude Code. It does not name alternative tools or exclusions, but the timing guidance is clear enough for an agent to know when to invoke it.

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