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

The description transparently discloses that the tool does not send anything over the network and does not expose the token itself, effectively communicating its read-only and privacy-preserving nature. This aligns with the annotations and gives additional context beyond basic metadata.

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—two sentences—and efficiently covers the tool's purpose, the information it provides, its side effects, and when to use it. There is no unnecessary verbosity; every sentence contributes to understanding the 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 lack of an output schema, the description sufficiently outlines what the tool reports (connection status, token source, expiry, file location) and when to invoke it. It also mentions the tool's non-destructive and non-network nature, providing enough context for a user to decide whether to call it.

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, so no parameter descriptions are needed. The description does not need to explain parameters, and the schema reflects this. The description is complete in this regard.

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: to show whether Yandex Metrika is connected, including token presence, source, expiry, and file location. It also provides a condition for when to call it (if Metrika tools report connection not configured). This is specific and 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?

The description explicitly says when to use the tool: 'Вызовите это, если инструменты Метрики отвечают, что подключение не настроено.' It also mentions that it does not send anything to the network and does not reveal the token, providing clear behavior expectations for users.

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 annotations declare idempotentHint=true, but the description states the code is one-time and that a rejected code requires calling start_login again for a fresh one. Repeated calls with the same arguments are therefore not idempotent, so the description contradicts the annotations.

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 dense sentences cover the workflow, file permissions, live verification, postcondition, and recovery path. The main action is front-loaded and every sentence adds value.

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 prerequisite, success condition, file permission, and failure recovery, which is strong for a one-parameter tool. However, the misleading idempotentHint leaves retry semantics ambiguous despite the one-time-code warning.

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 100%, so the schema already documents the code parameter. The description adds important meaning beyond the schema: the code comes from start_login, is valid for 10 minutes, and is single-use.

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 as the second step of connecting Metrika, specifying that it exchanges the confirmation code from start_login for an access token, saves it, and verifies it with a live request. This distinguishes it from siblings by naming its role and the exact operation it performs.

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 explicitly says this is the second step after start_login, explains that other tools become usable immediately after success, and tells the agent to call start_login again for a fresh code if the current code is rejected. This is clear when-to-use and recovery 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?

With annotations already covering read-only, idempotent, non-destructive, and open-world behavior, the description adds substantial context: default aggregation, totals semantics, total_rows, sampled/sample_share meaning, and the accuracy=full workaround for exact data. These are real behavioral traits not captured by the annotations.

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 dense but well front-loaded: purpose and default output come first, followed by dimensions, metrics, response semantics, and the required counterId. Every sentence carries useful information, and backticked names keep it readable despite the complexity.

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 13-parameter API tool with no output schema, the description covers the most important return semantics: default fields, totals, total_rows, and sampling. It does not summarize pagination, filters, date defaults, or the Direct login 403 behavior, but the 100%-covered input schema carries those details adequately.

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 baseline is 3. The description still adds useful semantics beyond the schema by explaining that dimensions turn the result into rows, giving conversion metric examples such as goal reaches/conversionRate, and identifying list_goals as the source of goal IDs.

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?

It states a specific verb and resource: querying the Yandex Metrica Reporting API (stat/v1/data) for a counter. It immediately distinguishes the default aggregated behavior from dimension-based row breakdown, so an agent knows exactly what the tool does.

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 usage context for dimensions, metrics, conversions, and sampling, and it points to list_counters and list_goals for required IDs. However, it does not explicitly say when to use this tool instead of sibling tools such as raw_request, nor does it state when not to use it.

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 returns counters with id, name, and site2, and that it supports a search filter. It does not contradict the annotations (read-only, idempotent, non-destructive). It does not mention rate limits or errors, but for a simple read operation this is acceptable. The behavior is transparent enough.

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, stating the main purpose in the first clause. It includes relevant supplementary information (id usage in other tools) without unnecessary verbosity. The structure is clear and free of redundancy.

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 operation, the description is complete. It explains what the tool returns (counters with specific fields) and mentions the search filter, which is the main functionality. Pagination is implicit via the schema's offset and perPage parameters. The lack of an output schema is compensated by the description of the returned fields. It is adequate for an agent to use 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?

The description adds little beyond the schema: the search parameter is described in the schema as a substring filter, and the description repeats that. The mention of counter fields (id, name, site2) is about the response, not parameters. Since schema coverage is 100% and descriptions are provided, the baseline is 3, and the description does not elevate 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 returns Yandex Metrika counters available to the token, and it specifically mentions that the id field is used in related tools (get_statistics and list_goals), which helps differentiate it from those tools. The verb 'Returns' is precise, and the resource is 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?

The description implies usage by stating that the id from counters is used in get_statistics and list_goals, suggesting this tool is a prerequisite for those operations. It also explains the search parameter for filtering. However, it does not explicitly state 'when to use this tool vs alternatives' in direct terms, but the context is sufficient.

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?

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so the safety profile is covered. The description adds chaining context (Management API, downstream metric names) but says nothing about the return shape, pagination, or whether all goals are returned — with no output schema, that is a real 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?

Three tight sentences: purpose first, then downstream usage, then the parameter prerequisite. Every sentence carries information, though the embedded metric-name syntax makes it slightly dense.

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 one-parameter read-only listing tool with no output schema, the description covers what it returns, why the caller needs it, and where the counterId comes from. Only the exact contents of the returned goal objects are left unexplained.

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 single counterId parameter has 100% schema coverage, including its own description pointing to list_counters, so the baseline of 3 applies. The description restates the requiredness and provenance but adds no meaning 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?

States a specific verb and resource: returns the goals/conversions configured on a Metrica counter via the Management API. It also distinguishes itself from siblings by naming list_counters as the source of counterId and get_statistics as the consumer of the returned goal IDs.

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?

Gives clear context: call it when you need goal identifiers to build conversion metrics like ym:s:goal<id>reaches in get_statistics, and get counterId from list_counters first. There is no explicit when-not-to-use or alternative listing tool, but the routing to both sibling tools is unambiguous.

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 annotations already indicate destructiveHint=true, but the description adds important context: what exactly is deleted, what remains (env token), and that the app's access stays active on Yandex's side. This exceeds what annotations provide alone.

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, each providing distinct value: the primary action, the exception, and the side effect. No fluff or redundancy.

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 no-parameter destructive action with robust annotations and no output schema, the description fully covers what an agent needs to know: what is affected, what isn't, and the external implication.

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?

There are zero parameters, so the description doesn't need to explain any. It correctly focuses on the effect of the operation itself.

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 that the tool removes the saved Metrika token from disk, and distinguishes it from the environment variable token. It uses precise language and is not a tautology.

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 explains what the tool does and explicitly mentions what it does NOT do (env var token). It doesn't explicitly compare to siblings like start_login or finish_login, but the context makes it clear this is a logout operation.

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?

Given destructiveHint=true and readOnlyHint=false in annotations, the description adds crucial context about write operations requiring confirmWrite=true, which is not in the annotations. It also mentions that POST/DELETE are write operations. However, it doesn't detail what is destroyed or the exact consequences, but the annotations already set the destructive nature, and the description adds a practical guard.

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, front-loaded with the purpose, and each sentence earns its place. It includes examples, usage conditions, and parameter behavior without redundancy. The structure is efficient and easy to scan.

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 5 parameters and nested objects, the description together with the schema provides adequate guidance. It lacks mention of response format, but there is no output schema, and for a generic request tool, response behavior is inherently variable. It covers the essential aspects: when to use, how authentication works implied, and write confirmation.

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. The description adds value by explaining the role of 'query' as query string parameters and 'body' as JSON for POST, which aligns with the schema. However, it doesn't add much beyond the schema for paths, method defaults, or confirmWrite requirements beyond what the schema describes.

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 is a universal request tool that directly accesses any path of the Yandex Metrica API, with concrete examples. It also explicitly notes it is for endpoints without a dedicated tool, distinguishing it from siblings like list_counters and get_statistics.

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 explicitly says when to use it (for endpoints without a dedicated tool) and implies when not to (when a dedicated tool exists). It also provides conditions for POST/DELETE requiring confirmWrite=true, and notes GET is free. This effectively routes agents to the right tool.

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 annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds meaningful behavioral context beyond those hints: no config editing or client restart is required, the code expires in 10 minutes, and the code can only be exchanged by this server. It does not discuss exact return URL format or error conditions, but the safety profile is already covered by annotations.

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?

Every sentence adds a distinct piece of information: the step's role, the no-config/no-restart property, the returned link, the exact user actions, the handoff to finish_login, the 10-minute validity, and the security property. There is no filler 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 zero-parameter first step of a two-step flow, the description is nearly complete: it explains what is returned, what the user must do, where the code goes next, and the code lifetime. It does not mention what to do if the user is already authenticated, but that is a minor gap given the sibling auth_status tool and the focused scope.

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, so the schema carries no burden; the baseline is 4. The description clarifies that no configuration changes are needed and focuses on the returned OAuth link rather than any inputs, which is sufficient for a parameterless tool.

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 'Первый шаг подключения Яндекс Метрики', naming a specific action (starting Yandex Metrika OAuth connection) and its resource. It also differentiates itself from the sibling finish_login by explicitly stating that the received code must be passed to finish_login, so an agent can distinguish the two halves of the flow.

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 user-facing instructions: show the full link, ask the user to open it in a browser under an account with access to the needed counters, confirm access, and send back the confirmation code. It also names the follow-up tool finish_login, making the intended sequence unambiguous.

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