LogsLoom

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

No annotations are provided, so the description carries the transparency burden. It usefully discloses the fallback behavior: trying a direct HTTP path that doesn't exist yet and then falling back to searching the latest list page. It stops short of noting potential limitations like older logs possibly not being on the latest page, but the core workaround is transparent.

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. The core purpose is front-loaded, and the second sentence provides valuable behavioral detail that earns its place.

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 single-parameter tool with no output schema and no annotations, the description covers the main operation and the fallback mechanism. It could mention what is returned or how failures are handled, but the current detail is adequate for an agent to call the tool with reasonable confidence.

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%, and the schema already documents the 'id' parameter as 'Log event id from ingest or search.' The description adds no parameter-level meaning beyond what the schema provides, so the 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 uses a specific verb and resource: 'Fetch one log by id.' This clearly distinguishes it from the sibling tools (logs_ingest, logs_search, logs_projects_list) without needing to inspect their 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?

The phrase 'Fetch one log by id' clearly communicates the intended use case: retrieve a single known log. It does not explicitly name alternatives or exclusions, but the by-id scoping is enough to guide an agent away from the search tool for this task.

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 discloses the POST endpoint (implying a write operation), the batch limit of 100, and the two modes. However, it doesn't disclose what happens on success/failure, whether the API is idempotent, or any rate limits. For a write tool with no annotations, this is adequate but not rich.

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, front-loaded with the core action and endpoint, then the key branching logic. Every word earns its place; no fluff or repetition of schema details.

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 7 parameters, no output schema, and no annotations, the description covers the essential decision (single vs batch) and the constraint (max 100). It doesn't explain return values or error behavior, but the core calling contract is clear enough for an agent to invoke it 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 only 43%, so the description must compensate. It does: it explains the relationship between 'level'+'message' (single event) and 'events' (batch), and notes that 'events' overrides single-event fields. This adds meaning beyond the raw schema, though it doesn't detail every optional field like service, metadata, timestamp, or environment.

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 ('Send'), a resource ('log events to LogsLoom'), and the exact endpoint (POST /v1/logs). It also distinguishes the two invocation modes (single event vs batch), which clearly separates it from sibling tools like logs_get and logs_search.

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 clear context for when to use this tool: to send one or many log events. It explains the single vs batch choice and the max batch size. It doesn't explicitly say when NOT to use it or name alternatives, but the sibling tools (get, search, projects_list) are obviously different operations, so the usage context is clear enough.

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 provided, so description carries the burden. It discloses a connectivity check via GET /v1/logs and that it returns project scope, implying a read-only operation. It also notes the absence of a user-session API. It does not detail error behavior or side effects, but for a simple GET that is acceptable.

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?

Single sentence, front-loaded with the core purpose ('Returns the project scope'), and includes only necessary context about the connectivity check and lack of an alternative API. 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?

Tool is simple with no parameters, but the description does not specify the exact structure of the returned project scope (e.g., string vs object) nor any error conditions. Given no output schema, this leaves ambiguity about the return value, making it incomplete for an agent that needs to interpret the result.

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?

Tool has zero parameters and schema coverage is 100% (vacuously). Baseline is 4 for zero-param tools. The description explains the tool's purpose and what it returns, which is sufficient given no parameters to document.

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?

Description states a specific verb 'returns' and resource 'project scope', and clearly distinguishes from sibling log tools (logs_get, logs_ingest, logs_search) by being about project scope rather than log operations. The title 'Current project scope' reinforces the purpose.

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?

Implies usage by noting 'There is no user-session projects API', indicating this is the only way to get project scope. It does not explicitly list alternatives or when-not-to-use, but the distinction from siblings is obvious from the name and purpose.

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 does state the operation is a GET and scoped to the API-key's project, which conveys a read-only, project-scoped behavior. However, it does not mention rate limits, auth requirements, result ordering, pagination behavior beyond filter names, or the response shape.

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 immediately states the action, scope, endpoint, and filters. There is no filler or redundant prose, though it is compact rather than rich in detail.

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 list-style tool with no output schema and no annotations, the description gives the endpoint, scope, and filter list, which is adequate for a basic call. It is not fully complete because it omits response format, ordering, pagination semantics beyond offset/limit, and sibling-tool routing, leaving the agent to infer those details.

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 already documents q, limit, offset, and service at roughly 80% coverage. The description names all five filters and adds 'message contains' semantics for q, but does not meaningfully enrich level or service beyond what the schema provides. This is consistent with 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 opens with a specific verb and resource: 'List logs for the API-key's project.' It also cites the endpoint and enumerates the available filters, making the tool's core function unmistakable. However, it does not explicitly distinguish this from sibling tools like logs_get or logs_ingest.

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?

There is no guidance on when to use logs_search versus logs_get, logs_ingest, or logs_projects_list, and no mention of when not to use it. The filter list implies a search use case, but alternatives and exclusion conditions are left entirely to inference.

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