Transkriba — Russian audio transcription

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 and does so well. It reveals the returned fields (upload_url, s3_key, content_type), the required PUT request with exact Content-Type, the 6-hour link lifetime, and the 5 GB file limit. It also explains that the tool itself is not the upload step but the first step in a two-step workflow.

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 well-structured and front-loaded: it states the target scenario, the return values, the upload workflow with a concrete curl example, and the constraints. Every sentence contributes useful information, and the example is compact but instructive.

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, so the description appropriately explains the return values and the complete workflow required to use them. It covers prerequisites, the upload method, the Content-Type requirement, the expected next step, and constraints like expiry time and file size. An agent has enough information to successfully invoke the tool and complete the intended operation.

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 file_name and content_type in sufficient detail. The description adds contextual constraints like the local-file requirement and the 5 GB limit, but it does not substantially improve parameter-level semantics 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 clear purpose: obtaining an upload URL for a local file that lacks a public link. It explicitly contrasts this with the public-link workflow by mentioning local files and routing the result to transcribe_file, which distinguishes it from siblings like transcribe_url.

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 clearly specifies when to use the tool: for a local file without a public link, and it outlines the follow-up step of passing s3_key to transcribe_file. It does not explicitly name the alternative for files with public links, such as transcribe_url, but the condition 'no public link' provides a clear contextual exclusion.

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. The verb 'Показывает' conveys a read-only, non-destructive query. It adds the practical caveat about checking before large batches, which is valuable behavioral context beyond the literal reading. Lacks explicit statements about permissions or exact return format, but for a simple balance check the risk is low.

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. The first states the core function and the second provides a practical usage hint. There is no redundancy or 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?

For a parameterless read-only query with no output schema, the description sufficiently conveys what the agent needs to know: it returns the remaining balance in minutes and suggests when to call it. It does not explicitly describe the return payload, but the meaning is clear enough for this simple tool.

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?

This tool takes zero parameters, so the schema covers everything. The description does not need to elaborate on inputs; the baseline of 4 for a no-parameter tool 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?

Description clearly states the tool shows the remaining transcription minutes on balance ('Показывает, сколько минут расшифровки осталось на балансе'), a specific verb and resource. It is immediately distinguishable from sibling tools like get_transcript or transcribe_file.

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?

Provides explicit guidance on when to call it: before a batch of long recordings to avoid hitting the limit mid-process. It does not name alternatives explicitly, but sibling contexts make it clear this is the balance query.

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 discloses the processing state (status=processing), the need for polling, the processing time ratio, and the conditional presence of speaker labels based on diarize=true. This is transparent about the tool's non-immediate response and conditions, though it does not mention error cases or side effects (which are minimal for a read-only fetch).

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 well-structured: two sentences (three in the original Russian) that front-load the main purpose, then add polling guidance and timing. No redundant information; every sentence contributes actionable 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?

For a simple polling tool with two parameters and no output schema, the description covers everything an agent needs: what it returns, how to handle the processing state, the expected duration, and the condition for speaker labels. It is complete for correct invocation and interpretation.

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 job_id and format, including the default text format. The description adds value by explaining that speaker labels only appear if diarize=true was passed at transcription time, which is a behavioral condition not in this tool's schema. This extra context helps the agent understand output variability, though it's not strictly about parameter syntax.

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 task state by job_id, and when ready, the transcript text with timestamps and speaker labels (if diarize=true). It uses a specific verb ('возвращает') and resource (task state/transcript), and implicitly distinguishes from siblings like list_transcripts by requiring a specific job_id for a single transcript retrieval.

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 polling guidance: while processing it returns status=processing, so call again after a pause. It also mentions the timing (an hour of audio takes ~1.5 minutes). However, it does not explicitly mention alternatives or when not to use this tool versus list_transcripts or search_transcripts, so it's clear on how to use but not on when to choose this over others.

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 disclosure burden. It clearly indicates a non-mutating, account-scoped list operation and names the output fields, which is good. However, it omits ordering, pagination, and default-limit behavior, leaving some transparency gaps.

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 no filler. The first sentence defines the operation and output fields, and the second provides a practical use case. Both sentences earn their place and the structure is 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 simple list tool with one optional parameter and no output schema, the description covers the essential return fields and gives a realistic invocation context. Minor details such as explicit sorting direction and pagination are not critical for a basic recent-items listing.

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%: the only parameter, limit, is already described in the input schema ('Сколько записей вернуть, до 50'). The description adds no extra parameter meaning, 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 concrete verb ('Перечисляет') and specifies the resource ('последние расшифровки учётной записи') plus the returned fields: id, file name, duration, and status. It is clear and actionable, though it does not explicitly differentiate itself from the sibling search_transcripts beyond the word 'последние'.

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 a concrete use case: finding the job_id of yesterday's recording without asking a human. This makes the intended context clear, but it does not state when to prefer alternatives like search_transcripts or get_transcript.

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 discloses the scope (all ready transcripts), the result fields (timestamp, speaker name), and gives a practical example. It does not mention rate limits or cost, but the behavior is clear enough for a read-only search 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?

Two short sentences with no fluff. The first sentence states the core behavior and output, the second adds a concrete usage scenario. Every sentence 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 simple two-parameter search tool with no output schema, the description adequately covers scope, return format, and intended use. It does not detail exact result structure beyond timestamp and speaker name, but that is sufficient for selection and invocation.

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 explains both parameters: query is what to search, limit caps matches at 50. The description adds the nuance of searching a phrase and returning match context, but does not significantly extend parameter 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?

The description states a specific verb and resource: it searches a phrase across all ready transcripts and returns matches with timestamp and speaker name. This clearly distinguishes it from siblings like list_transcripts and get_transcript.

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 a concrete usage context: finding where a topic was discussed in a long conversation without rereading everything. It does not explicitly name alternatives or exclusion conditions, so it stops short of full routing 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 no annotations provided, the description carries the full burden. It discloses key non-obvious behaviors: the operation returns immediately (async), the result must be fetched via get_transcript, and minutes are deducted from the balance based on recording duration. These go well beyond the basic transcription purpose.

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 three concise sentences with no redundancy: purpose, async workflow, and cost implication. Each sentence earns its place and the core purpose is front-loaded.

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 4 documented parameters and no output schema, the description sufficiently covers the return value (job_id), the surrounding pipeline (create_upload and get_transcript), and the billing side effect. An agent has enough information 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 100%, so the schema already documents all four parameters. The description adds no new parameter details beyond restating that the file comes from create_upload, which matches the s3_key schema description. Baseline 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 states a specific verb and resource: it converts to text a file uploaded via create_upload. It also differentiates from the sibling transcribe_url by explicitly tying input to the upload flow, and clarifies the async return of a job_id.

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 clearly establishes the workflow: use after create_upload and retrieve results via get_transcript. It implies the distinction from transcribe_url by naming the upload source, though it does not explicitly state alternatives or exclusions.

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 discloses the async job_id pattern, cost implications (balance deduction based on duration), and diarize behavior. This is substantial transparency, though it omits failure modes and authentication requirements, which would elevate to 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?

Three focused sentences cover purpose, key option, and behavioral notes. Front-loaded with the main action and outcome. No wasted words, though slightly dense in the third sentence.

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 async result retrieval and billing side-effects, the description covers the critical flow: submit URL, optionally diarize, get job_id, fetch via get_transcript, and note cost. Missing details like error handling and output format are minor given the tool's simplicity.

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 covers all 3 parameters at 100% with decent descriptions. The description adds clarity on diarize (speaker separation) but adds nothing about language or url beyond schema. Meets baseline without significant added value.

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 clearly states the verb (transcribe) and resource (audio/video via URL) with output (text with timestamps). It distinguishes from siblings by naming get_transcript as the retrieval step and implicitly contrasting with file-based transcription (transcribe_file). Purpose 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?

Specifies supported platforms (YouTube, VK Video, Rutube, etc.) and directs agents to use get_transcript to fetch results. However, it does not explicitly exclude alternatives like transcribe_file for local files or create_upload for uploads, leaving some ambiguity for edge cases.

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