OneStepTranscribe MCP server
Server Details
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.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Glama MCP Gateway
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.
Tool Definition Quality
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.
Available Tools
4 toolsbuy_creditsAInspect
Get 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). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses that payment occurs outside the chat, credits are added to the specified email, and can be used later. It could mention prerequisites (e.g., user logged in) but adequately explains the asynchronous, non-destructive behavior.
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 unnecessary words. Each sentence adds value: purpose, payment flow, and post-purchase use. Front-loaded effectively.
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 2-parameter tool with no output schema, the description fully covers what the tool does, how it works, and how results are used. Complete for effective agent 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 describes both parameters. The description reinforces the email parameter's role and default bundle value but adds little beyond the schema. 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 clearly states the tool's action: 'Get a link to buy transcription credits.' It specifies the resource ('transcription credits') and verb ('buy'), and implicitly distinguishes from siblings like check_credits, prepare_upload, 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?
The description explains the payment flow (secure checkout in browser, not in chat) and post-purchase integration with other tools, providing context for when to use. However, it does not explicitly state when to avoid using or compare directly to siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
check_creditsAInspect
Check 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. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It explains the query action and cost, but does not specify the return format or behavior on insufficient credits, though this is a minor 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 no extraneous information, efficiently conveying purpose and usage 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?
Complete for a simple query tool, but could mention the return value (number of credits) given no output schema.
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 100% of the parameter with a description; the tool description adds no extra semantic value 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 clearly states the tool checks transcription credits for a given email, specifies the cost per transcription, and distinguishes it from the sibling tool prepare_upload.
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 notes that prepare_upload already checks credits, indicating when this tool is an alternative and when it might be redundant.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
prepare_uploadAInspect
Step 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). |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses key behaviors: server never receives the file, upload is direct to S3, URL expiration, and that it checks credits. This is comprehensive for a mutation 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?
The description is relatively long but every sentence serves a purpose. It front-loads the core purpose and then provides step-by-step guidance. Minor redundancy could be trimmed, but overall effective.
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 complexity (5 parameters, no output schema, part of multi-step process), the description covers the workflow, constraints (expiration), and post-steps. It lacks detailed return structure, but the mention of 'returns a one-time command' provides enough context for an 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?
Schema descriptions cover 100% of parameters, so baseline is 3. The description adds value by explaining how to obtain filesize (stat/wc), that file_path is used to build the curl command, and that referrer earns credit. This extra context justifies a 4.
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 'Step 1 of transcribing a file' and explains its role: verifies credits, creates order, returns upload command. This distinguishes it from sibling tools like start_transcription (step 4) and sets clear expectations.
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 describes the sequence: after calling this, upload file directly, then call start_transcription. Also warns that upload URL expires in 1 hour, guiding immediate action. However, no explicit 'when not to use' or alternative scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
start_transcriptionAInspect
Step 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. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description fully bears the burden. It discloses key behaviors: re-checks credits, verifies audio upload on S3, rejects if missing, consumes one credit, and emails four result files. This is comprehensive.
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, consisting of three clear sentences with no filler. It front-loads the purpose and then adds behavioral details efficiently.
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?
While the description covers the workflow and side effects, it does not specify the synchronous return value (e.g., status or new order_id). Given no output schema, the agent may need to know what the tool returns immediately.
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%, but the tool description adds context for both parameters: email is 'the same email used in prepare_upload (re-checked for credits)' and order_id is 'returned by prepare_upload'. This reinforces the workflow context 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 explicitly states it is 'Step 2 of transcribing a file' and that it starts the job after re-checking credits. It distinguishes from sibling tools by referencing prepare_upload and indicating this is the subsequent step.
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 implies it should be used after prepare_upload and re-checks credits. It provides usage context but does not explicitly state when not to use or mention alternatives. Given the sibling set, alternatives are clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!