Channel Brains

by Pu11en

Overview Schema Related Servers Score Discussions

Python

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Server Quality Checklist

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Latest release: v0.1.2
Disambiguation5/5
Each tool targets a distinct operation: creation, status, listing, search, transcript retrieval, and deletion. No two tools overlap in purpose, so agents can easily distinguish them.
Naming Consistency5/5
All tools follow a consistent verb_noun snake_case pattern (e.g., create_brain, delete_brain, get_video_transcript). The repeated verb 'get' is used clearly for different resource types.
Tool Count5/5
Six tools form a compact, focused set for a YouTube caption indexing server. Each tool serves a clear role without bloat or redundancy.
Completeness5/5
The server covers the full lifecycle: create (index) a brain, check status, list videos, search captions, retrieve transcripts, and delete. No essential operation appears missing for its stated purpose.
Average 3.8/5 across 6 of 6 tools scored. Lowest: 3.1/5.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 1 community issues answered or closed in the last 6 months
- 11 commits in the last 12 weeks
- Last stable release on August 3, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden for behavioral disclosure. It only states the action is to queue or resume indexing, without revealing side effects, permissions, idempotency, or potential impact on existing data. This is insufficient for a mutation-like tool.
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 a single concise sentence with no redundant words. It front-loads the action clearly and is appropriately sized for the information it conveys.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has three parameters and no annotations, the description is too sparse. It omits parameter semantics, call behavior (e.g., what happens on repeated calls), and prerequisites. Although an output schema exists, the description still leaves significant gaps for a mutation tool.
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?
The schema description coverage is 0%, and the description does not mention any of the three parameters (channel_url, language, max_videos). The description adds no clarity about how these parameters influence the indexing behavior, leaving the agent without guidance.
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 action ('Explicitly queue or resume') and the resource ('local channel caption indexing'), which is specific and distinguishes it from sibling tools that handle status, listing, search, transcript, and deletion. The name 'create_brain' is ambiguous, but the description resolves it by explaining the actual behavior.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'Explicitly queue or resume' implies this tool is for manually triggering indexing, but it does not state when to use it versus alternatives, nor does it mention prerequisites or exclusions. There is no explicit guidance on when to prefer this over sibling tools like get_brain_status.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses two important behavioral constraints: it never generates an answer and never contacts YouTube. However, it omits other behavioral details such as pagination, sorting, or error handling, so transparency is adequate but not rich.
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 no filler. Both sentences convey meaningful constraints and purpose, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has three parameters and no annotations, yet the description offers minimal guidance. Even with an output schema, the lack of parameter semantics and usage context leaves the agent under-equipped to invoke the tool correctly.
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 0%, and the description provides no explanation of query, limit, or brain_id. The agent must infer semantics solely from parameter names and defaults, which is insufficient.
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 returns ranked, timestamped caption evidence, which is a specific verb+resource combination. It also explicitly distinguishes itself from answer-generating tools and YouTube contact, differentiating it from siblings like get_video_transcript.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides implied context (retrieval-focused, not answer generation) but gives no explicit when-to-use guidance or named alternatives. It doesn't say when to prefer search_brain over list_brain_videos or get_video_transcript.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It does add value by stating 'Never contacts YouTube', which is a meaningful constraint. However, it does not mention permissions, side effects, or other behavioral traits, so disclosure is only partial.
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 extremely concise, consisting of two short sentences. Both sentences contribute meaning: the first states the operation and target, the second adds a key constraint. No filler or redundancy.
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 tool is simple (one required param, output schema present), and the description adequately describes what the tool lists and that it doesn't contact YouTube. However, it lacks usage context and does not clarify what 'selected' means or how this relates to the brain_id parameter, leaving some ambiguity for the agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description provides almost no parameter detail. 'Page through' hints at limit/offset, and 'selected videos' may imply brain_id selection, but the description does not clearly map each parameter or explain the required brain_id semantics.
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 uses the specific verb 'Page through' and clearly identifies the resource as 'selected videos and outcomes', distinguishing it from siblings like search_brain and get_video_transcript. The additional 'Never contacts YouTube' clarifies that this is a local listing operation, not an external API call.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no explicit guidance on when to use this tool versus search_brain or get_video_transcript. It implies a listing use case but does not state exclusions or alternatives, leaving the agent to infer the appropriate context from the name and siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It discloses a key behavioral trait: 'Never contacts YouTube,' indicating a local read operation with no network calls. It also implies pagination via 'Page through,' but doesn't address error handling, auth, or what happens if the video isn't indexed.
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 short sentences, with the main action in the first sentence and a critical caveat in the second. It's front-loaded and contains no filler.
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?
For a simple paginated getter, the description covers the core behavior and an important limitation (never contacts YouTube). The presence of an output schema covers return shape details, and the required parameters are in the schema. However, it lacks context about what a 'brain' is and how video_id relates to the index.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% and the description provides no explicit parameter explanations. 'For one video' hints at the video_id parameter and 'Page through' hints at limit/offset, but brain_id is left unexplained. The description does not compensate for the lack of schema descriptions.
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 uses a specific verb 'Page through' and identifies the resource as 'indexed caption chunks for one video.' It clearly distinguishes this from sibling tools like search_brain or list_brain_videos by focusing on retrieving transcript content for a single video.
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 clear context that this tool retrieves captions from an index, and the statement 'Never contacts YouTube' implies it's the offline/local option compared to potential live fetch alternatives. However, it doesn't explicitly say when to use it over search_brain or other siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It discloses that deletion is permanent and requires confirm=true, which are critical behavioral traits for a destructive operation.
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 a single sentence, front-loaded with the verb and resource, containing no redundant words.
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 simplicity, an output schema exists, and the description covers the permanent nature and confirmation gate. It lacks ownership/permission details but is sufficient for basic usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must compensate. It clarifies that confirm must be true to execute deletion, adding meaning beyond the schema's default false. brain_id is implicit via 'one brain' and the schema title.
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 'permanently delete one brain' which is a specific verb+resource. It distinguishes from sibling tools like create, list, search, and transcript.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The purpose is implied and the confirm=true requirement is a clear usage condition, but there is no explicit comparison to alternatives or when not to use this tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations supplied, the description carries the burden of behavioral disclosure. It discloses that it reads (read-only), works locally without contacting YouTube, and handles one or all brains based on the parameter. This is valuable context, though it doesn't discuss potential errors, performance, or data formats; however, the output schema covers return structure, so this is sufficient.
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 short sentences. The first front-loads the action and scope; the second adds a crucial constraint. Every word earns its place with no fluff or repetition of schema information. It is concise and well structured.
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 tool's simplicity (one optional parameter) and the presence of an output schema, the description covers the essential information: purpose, parameter semantics, and a key behavioral trait (no YouTube contact). The output schema handles return values, and sibling tools provide broader context, so the description is complete for an agent to invoke correctly.
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?
The schema has one optional parameter with zero description coverage, but the description explicitly explains the parameter's effect: providing a brain_id reads one brain, while omitting it reads all local brains. This fully compensates for the schema's lack of detail and gives the agent clear guidance on how to use the parameter.
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 reads progress (specific verb) for either a single brain or all local brains (resource and scope). It distinguishes from siblings by focusing on progress/status rather than creation, listing, search, or deletion, and adds a unique behavioral note (never contacts YouTube).
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 provides clear context: use this to read progress for one or all local brains, without contacting YouTube. It implies when to choose this over alternatives (when you need status) and gives a constraint (never contacts YouTube), but it doesn't explicitly mention alternatives or exclusions, so it falls short of a 5.
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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