F
license-
qualityD
maintenanceThis MCP server fetches and extracts transcripts from YouTube videos, enabling AI language models to access and analyze video content.
1
goodbotbad.bot
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Server Details
The MCP server behind goodbotbad.bot, where the crowd rules AI transcripts good bot or bad bot.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
A4.1/5.0
Tool DescriptionsA
Average 4.3/5 across 4 of 4 tools scored.
Server CoherenceA
Disambiguation4/5
The tools have distinct purposes: complete_pair handles the pairing workflow, get_post retrieves individual posts, search_posts searches archives, and submit_transcript handles submissions. There is slight potential confusion between get_post and search_posts, but their descriptions clearly differentiate (single post vs. search results).
Naming Consistency4/5
Tool names follow a consistent verb_noun pattern (complete_pair, get_post, search_posts, submit_transcript). The only minor deviation is the lack of a consistent action prefix for pair operations, but overall naming is predictable and clear.
Tool Count5/5
With 4 tools, the set is lean and focused on the core workflows of posting, searching, viewing, and pairing transcripts. Each tool serves a distinct and necessary function for the server's purpose.
Completeness3/5
The tool set covers the main workflows for submitting and reviewing transcripts, but there are notable gaps: there is no tool to vote on drafts, manage user accounts, or handle moderation tasks beyond pairing. The server's description mentions moderation approval, yet no tool exposes that action directly.
Available Tools
4 toolscomplete_pairAnswer the site's request for a post's other halfAInspect
goodbotbad.bot pairs transcripts: two posts on one subject where exactly one thing differed and the outcomes were opposite. Call this with no arguments to be handed an unpaired post's prompt to run yourself. Call it with id and the response you got to stage the other half. NOTHING IS PUBLISHED — staging creates a draft and a pair candidate, and both a human and a moderator have to agree before either appears.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| id | No | The post you are answering. Omit to be given one. | |
| note | No | ||
| model | No | Which model produced your response. | |
| verdict | No | ||
| response | No | What the model produced when you ran its prompt. |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| id | No | The post to run, when you asked for one. |
| side | No | Which way that post was ruled, so you know which half is missing. |
| prompt | No | Its prompt, verbatim. Run this. |
| pair_id | No | The pair candidate, or null where the draft stood but the pair claim did not. |
| draft_url | No | Where a human confirms what you staged. Nothing is published before that. |
Tool Definition Quality
A4.7/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate readOnlyHint=false, destructiveHint=false, and openWorldHint=true. The description adds crucial behavioral context: staging creates a draft and pair candidate, requiring both human and moderator approval before publication. This goes beyond annotations to explain the non-immediate publishing behavior and moderation gate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is four concise sentences, each earning its place: it explains the purpose, provides two usage scenarios, and clarifies the staging/moderation process. No fluff or redundancy. Front-loaded with the core concept of pairing transcripts.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema (documenting return values) and the annotations (readOnlyHint, destructiveHint, openWorldHint), the description sufficiently covers the workflow, moderation gate, and usage patterns. The agent has enough context to correctly select and invoke the tool, including the staging behavior not present in structured data.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 60% (3 of 5 params have descriptions). The description adds context for id and response by explaining their role in the staging workflow, but does not mention the note or verdict parameters, which lack schema descriptions. While the description compensates partially, the missing parameters leave a gap for the agent to understand their purpose.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: pairing two posts on a subject where one thing differed and outcomes were opposite. It distinguishes itself from siblings (get_post, search_posts, submit_transcript) by specifically handling the 'other half' submission workflow. The title 'Answer the site's request for a post's other half' reinforces the specific verb-resource relationship.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on two usage scenarios: call with no arguments to receive a prompt, or call with id and response to stage the other half. It also clarifies that nothing is published immediately, explaining the moderation workflow. This thoroughly informs when and how to use the tool versus alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_postRead one post in fullARead-onlyInspect
Fetch one post from goodbotbad.bot by its id: the full transcript, the submitter's note, the verdict and the vote counts. Use it to see exactly what was submitted before offering the other half of a pair, or to quote it back to your human.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | The post's public id. |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| id | Yes | |
| note | No | The submitter's own writing. Never machine-authored. |
| side | No | |
| fixes | Yes | Repairs offered against this post. Candidates, not rulings. |
| model | No | The catalogue slug, or null where the named model resolved to no row. |
| title | No | |
| turns | Yes | |
| votes | Yes | |
| repairs | No | If this post is itself a fix, the id of the failure it repairs. |
| category | Yes | |
| extraction | No | |
| provenance | Yes | |
| rerunnable | Yes | Its prompt turns are present and unelided, so complete_pair can re-run it. |
| accepted_fix | No | The one repair the author accepted, or null. The only one to act on unasked. |
| reported_model | No | What the submitter called it, verbatim. |
| verdict_declared | No | The lane the submitter entered it in, which the crowd may since have overturned. |
Tool Definition Quality
A4.2/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds valuable behavioral context by detailing exactly what data is fetched (full transcript, submitter's note, verdict, vote counts) and implying it is a safe, idempotent read operation. No annotation contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with zero waste. The first sentence front-loads the core purpose and return data; the second provides usage guidance. Every phrase earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has only one parameter and an output schema (though not shown), so the description doesn't need to explain return values. It lists all key return fields and provides concrete usage scenarios. It is complete for this simple tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and schema already describes 'id' as 'The post's public id.' The description only says 'by its id', which adds no new meaning 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.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Fetch' and the resource 'one post from goodbotbad.bot by its id', listing the specific data returned (full transcript, submitter's note, verdict, vote counts). It implicitly distinguishes from siblings by tying usage to 'offering the other half of a pair', which relates to the sibling 'complete_pair'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit usage scenarios: 'to see exactly what was submitted before offering the other half of a pair, or to quote it back to your human.' This provides clear context for when to use the tool, though it does not explicitly exclude alternatives or 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.
search_postsSearch the archiveARead-onlyInspect
Search goodbotbad.bot for transcripts people have already ruled on. Useful before submitting — a failure that is already in the archive should be voted on rather than posted again. Returns ids, titles, verdicts and vote counts.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| side | No | ||
| limit | No | ||
| model | No | A model slug, such as claude-opus-5. | |
| query | No | Words from the title or the submitter's note. | |
| category | No |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| results | Yes | Matching posts, most relevant first, capped at 25. |
Tool Definition Quality
A3.8/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint (true) and openWorldHint (true). The description adds that it searches an archive of ruled transcripts and returns specific fields (ids, titles, verdicts, vote counts). This provides some behavioral context beyond annotations, but does not discuss limitations, pagination, or search semantics. The additional value is moderate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three sentences, front-loaded with the core action, followed by a usage tip and return fields. Every sentence adds value without redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers purpose, usage guidance, and return fields. However, it does not explain the available search dimensions (side, category, model, limit) or how to combine parameters. With 5 optional parameters and low schema coverage, the description is not fully complete for an agent to craft effective queries without additional inference.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 40% (only 2 of 5 parameters have schema descriptions). The tool description does not mention any parameters or explain their meaning. Since coverage is low (<50%), the description must compensate but fails to do so, leaving the agent without guidance on how to use the parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches an archive of transcripts people have already ruled on, using the specific verb 'search' and resource 'transcripts/archive'. It distinguishes from siblings like submit_transcript by noting it is useful before submitting, implying it is for searching existing records.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Useful before submitting — a failure that is already in the archive should be voted on rather than posted again.' This tells the agent when to use this tool (before submit_transcript) and provides a clear usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
submit_transcriptSubmit a transcript for the crowd to rule onAInspect
Submit an AI prompt and the response it produced to goodbotbad.bot, where people vote good bot or bad bot on it. NOTHING IS PUBLISHED BY THIS CALL. It creates a draft and returns a URL; a human has to open that URL and confirm before anything becomes visible, and you should show them the URL. There is deliberately no note parameter — the note is the human's account of why the exchange is worth posting, they write it on that page, and it is not something to compose for them. Secrets and API keys are detected and the submission is refused outright. Personal data — an email address, a phone number, a home directory path — is redacted before storing and named in the reply; show your human what was removed along with the URL. Quote the prompt verbatim if you can; if the real input was too large or is not recoverable, describe it in objective instead and say so. If the transcript is larger than 65536 bytes, send manifest INSTEAD of turns — a role, a byte count and a short label per turn, and no bodies — and the server will ask your human which passages to send. Do not send the bodies and let the server reject them: that has already disclosed them. To submit a repair for a failure already on the site, pass its id as fixes — a fix is a rewritten prompt and the better response it produced, so its verdict is good, and it waits on two people rather than one: a moderator screens it, and whoever posted the failure decides whether it repairs it. If the reply asks for token usage, it is asking your human and not you — they read it off their own client, which you cannot see. Put what they say in inputResponses.usage and send the whole call again with the requestState; it attaches to the draft that already exists rather than making a second one. Never supply a figure of your own, and skipping is a fine answer.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| fixes | No | The id of a published post the crowd ruled bad bot, if this transcript is the repair of it. The id search_posts and get_post return. | |
| model | Yes | The model or product that produced it, as its own name. | |
| title | Yes | ||
| turns | No | The exchange in order. Omit when sending a manifest. Use role `attachment` for an image or file that was part of the exchange — its body describes what was there, since only text is stored. | |
| usage | No | Token counts, if your client has genuine API metadata. Omitted is better than guessed. | |
| verdict | Yes | Your human's ruling, not yours. | |
| category | Yes | ||
| manifest | No | Describes an oversized transcript without sending it: role, bytes and a short label per turn. | |
| source_url | No | ||
| requestState | No | Echo back untouched from a previous inputRequired reply. | |
| inputResponses | No | Answers to a previous inputRequired reply. |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| fixes | No | The id of the failure this repairs, if it is a repair. |
| state | No | `draft` on submission. Later legs report where the post has since got to. |
| draft_url | No | Where your human confirms it. Nothing is published until they do. |
| public_id | No | |
| redactions | No | What was redacted before storing, named so your human knows what changed. Empty when nothing was. |
| usage_source | No | `attested` when a human typed the figures. Null when they declined. |
Tool Definition Quality
A4.5/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate readOnlyHint=false (writes), openWorldHint=true (external interaction), destructiveHint=false. The description correctly and thoroughly explains behavioral traits: nothing is published immediately, secrets auto-rejected, personal data redacted, oversized transcripts handled via manifest. One minor gap: it doesn't explicitly state that the server enforces size limits, but the manifest workaround is described. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is dense and comprehensive but notably long. Every sentence earns its place for a complex multi-step tool, yet it could benefit from clearer sectioning or bullet-like formatting for quicker parsing by an AI agent. The front-loading is acceptable (core submission action first), but the wall of text reduces scanability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's high complexity (11 parameters, nested objects, multiple workflows) and presence of an output schema, the description covers almost all critical behavioral paths: draft creation, confirmation URL, secret detection, manifest flow, repair submission, token handling. Slight lack on what the output schema contains (the reply structure) but output schema itself provides that.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 73%, but the description vastly enriches understanding of parameters like manifest vs turns, fixes lifecycle, usage (human-provided, not AI-generated), and inputResponses. It explains roles not obvious from schema (e.g., attachment for non-text content) and nuances like quoting verbatim vs using objective.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool is for submitting an AI prompt-response pair to goodbotbad.bot for crowd voting. It distinguishes itself from siblings like search_posts/get_post by explaining the submission lifecycle (draft creation, human confirmation step).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides extensive when-to-use guidance, including when to send manifest instead of turns for oversized transcripts, how to handle repairs via fixes, and how to respond to inputRequired replies. It also clarifies when NOT to send bodies (to avoid disclosure) and distinguishes this tool from the human's own token logging.
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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