Connect the OneStepTranscribe MCP server to your AI assistant and turn audio or video into text without leaving the chat. It is a remote server, so there is nothing to install, no API key, and no account. Just add one URL and ask your assistant to transcribe a file.
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Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 4.4/5 across 4 of 4 tools scored.
Each tool serves a clear, non-overlapping purpose: credit purchase, credit check, upload preparation, and transcription start. No ambiguity.
All tool names follow a consistent verb_noun pattern (buy_credits, check_credits, prepare_upload, start_transcription) using underscores.
Four tools perfectly cover the core workflow (credit management + upload/transcript) without unnecessary extras.
The workflow is covered end-to-end, but there is no tool to poll transcription status or retrieve results (they are emailed). This minor gap could be addressed with a status tool.
buy_creditsAInspectGet a link to buy transcription credits. Payment happens through a secure checkout in the user's browser; it cannot be completed inside the chat. After buying, the credits are added to the email entered at checkout and can be used with prepare_upload / start_transcription.
| Name | Required | Description | Default |
|---|---|---|---|
| Yes | The email the credits should be added to. | ||
| bundle | No | How many credits to buy: 1, 5, or 10 (default 1). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Discloses that the tool returns a link, payment is external, and credits are later usable. Adequately explains the asynchronous nature.
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 covering purpose and workflow with zero unnecessary words. Front-loaded and efficient.
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 two simple parameters and no output schema, the description sufficiently explains the tool's behavior. Could mention the format of the returned link, but not essential.
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 description adds value by clarifying the email's role for credit assignment. Bundle parameter is described in schema and context is provided.
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?
Clearly states the tool provides a link to buy transcription credits. Distinguishes from siblings by describing its role in the workflow (prepares credits for prepare_upload/start_transcription).
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?
Explicitly states payment happens externally and cannot be completed in chat, and that credits are added to the provided email. Lacks explicit 'when not to use' but adequately guides appropriate usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
check_creditsAInspectCheck how many transcription credits the given email address has on onesteptranscribe.com. Each transcription costs one credit. Optional: prepare_upload already checks this for you before starting.
| Name | Required | Description | Default |
|---|---|---|---|
| Yes | The customer's email address. Results are sent here. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description clarifies that the tool is a read-only check (no destructive effects) and that each transcription costs one credit. It does not mention rate limits or auth, but the behavior is simple and adequately covered.
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 long, with the primary purpose in the first sentence and optional context in the second. No redundant information is present.
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 sufficient for a simple read-check tool with one parameter and no output schema. However, it does not describe what the tool returns (the number of credits), which would be helpful for the agent.
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 fully describes the 'email' parameter with format and pattern. The tool description adds no additional meaning beyond what the schema provides, meeting 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 clearly states the verb 'Check' and the resource 'transcription credits' for a given email address, making the tool's purpose unambiguous. It also distinguishes from siblings by noting that prepare_upload already performs this check.
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 hints that prepare_upload might make this tool redundant, but it does not explicitly state when to use check_credits versus alternatives like buy_credits or start_transcription. The guidance is minimal and implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
prepare_uploadAInspectStep 1 of transcribing a file. Verifies the account has credits, then creates the order and returns a one-time command to upload the audio. IMPORTANT: this server never receives the file. After calling this you (the assistant) must upload it directly from the user's machine: (1) get the file size in bytes — stat -f%z <file> (macOS), stat -c%s <file> (Linux), or wc -c < <file> — and pass it as filesize; (2) pass the absolute file_path; (3) run the returned curl command to PUT the file straight to S3; (4) call start_transcription with the same email and the returned order_id. The upload URL expires in 1 hour, so upload immediately.
| Name | Required | Description | Default |
|---|---|---|---|
| Yes | The customer's email address. The transcript is emailed here. | ||
| filesize | Yes | Exact size of the audio file in bytes (run `stat`/`wc -c` first). | |
| referrer | No | Optional referrer email (earns the referrer a credit). | |
| file_path | No | Absolute path to the local audio file, used to build a ready-to-run upload command. The file is never read by this server. | |
| ai_summary | No | Optional: also generate an AI summary (title, key points, chapters). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, but the description compensates fully. It discloses that the server never receives the file, that the upload URL expires in 1 hour, and that the assistant must handle the upload directly. This goes beyond typical transparency and ensures the agent understands the tool's side effects and constraints.
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 with the tool's role. Every sentence provides necessary information without redundancy. The inclusion of a clear workflow and IMPORTANT notes ensures efficiency for the agent.
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 no output schema, the description adequately covers the return value (a curl command) and the overall workflow. It could be improved by explicitly stating the response structure or fields, but it is still complete enough for the agent to execute the next steps.
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 baseline is 3. The description adds significant value by explaining how to obtain filesize (stat/wc commands), that file_path is used to build a ready-to-run upload command, and the role of email and optional parameters. This procedural detail goes beyond schema descriptions.
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 'Step 1 of transcribing a file' with specific actions: verifying credits, creating an order, and returning a one-time upload command. It effectively distinguishes itself from sibling tools like buy_credits, check_credits, and start_transcription.
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 step-by-step instructions after calling the tool, including getting file size, passing file_path, executing the returned curl command, and calling start_transcription. However, it could improve by explicitly stating when NOT to use this tool or noting alternatives, such as checking credits first.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
start_transcriptionAInspectStep 2 of transcribing a file. Re-checks that the account has credits and starts the job. The backend verifies the audio was actually uploaded to S3 (via the curl command from prepare_upload) and rejects the request if it is missing. This consumes one credit. The four result files (PDF, Markdown, DOCX, CSV) are emailed to the address when processing finishes.
| Name | Required | Description | Default |
|---|---|---|---|
| Yes | The same email used in prepare_upload (re-checked for credits). | ||
| order_id | Yes | The order_id returned by prepare_upload. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses credit re-check, S3 validation, credit consumption, and email delivery of four result files. It does not cover error states but provides substantial behavioral detail.
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 purpose ('Step 2'). No redundant information; each sentence contributes essential 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?
The description covers the workflow, parameters, and outcome (email sent). Without an output schema, it could mention the immediate return value, but given low complexity (2 params), it is fairly 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 coverage is 100% with descriptions, but the tool description adds context: email must match prepare_upload, and order_id is the one returned by prepare_upload. This adds meaning beyond the schema 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 clearly states this is 'Step 2 of transcribing a file' with a specific verb ('starts') and resource ('job'), distinguishing it from siblings like prepare_upload (step 1) and buy_credits/check_credits (credit management).
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 after prepare_upload by referencing the S3 upload verification and order_id from that step. It gives context for credit consumption but does not explicitly state when not to use or provide exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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