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amyodov

io.github.amyodov/yet-another-agentic-chat

Server Quality Checklist

75%
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  • Latest release: v0.5.1

  • Disambiguation5/5

    list_channels and join_channel are completely distinct: one is a read-only observation of available channels, the other is a state-changing action to enter or create a channel. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    Both tool names follow the same verb_noun pattern with snake_case: list_channels and join_channel. This is clean, predictable, and consistent.

    Tool Count2/5

    For a chat-oriented server, exposing only two tools is far too thin. The join_channel description implies that core messaging tools like send and check_inbox are needed after joining, but they are not part of the exposed tool set, leaving the server's actual surface severely underpowered.

    Completeness1/5

    The tool set has no way to send messages, receive messages, see other participants, or leave a channel after joining. Even though join_channel mentions these capabilities, they are not actual tools in the server, so an agent can join a channel but cannot communicate, making the surface effectively a dead end.

  • Average 4.7/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 103 commits in the last 12 weeks
    • Last stable release on
    • 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.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

  • Add a glama.json file to provide metadata about your server.

  • This server has been verified by its author.

  • Add related servers to improve discoverability.

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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

  • Behavior5/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    The description richly discloses behavior beyond the annotations: joining creates the channel when empty, nothing is pushed so check_inbox must be called every turn, and returned credentials must survive compaction. It also explains the recovery path by rejoining with the same channel and name. This compensates fully for the sparse annotation hints.

    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 long but every sentence carries essential operational information: confirmation requirements, creation behavior, polling commitment, credential persistence, and recovery. It is front-loaded with the core action and then escalates through consequence, so the length is justified.

    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 complexity and high-stakes persistent state, the description is comprehensive: it covers prerequisites, side effects, return-value handling, failure recovery, and downstream dependencies. The output schema exists, so not explaining return fields in the description is acceptable.

    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 100%, so the baseline is 3. The description adds meaningful context by tying channel/name to identity and membership recovery, and by warning that peer_secret must be preserved. It does not fully map the optional peer_uid/peer_secret parameters, but the schema already documents those clearly.

    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 operation: joining a channel on YAAC as a specific name, including the channel-creation behavior when empty. It distinguishes itself from the sibling list_channels by focusing on the act of going on air rather than merely enumerating channels.

    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 guidance: ask the user to confirm channel and name, never invent a name, and re-call if credentials are lost. It does not explicitly contrast with list_channels, but the guidance is contextually sufficient for calling this tool correctly.

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

  • Behavior5/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    Annotations already declare readOnlyHint=true and openWorldHint=false, and the description adds valuable behavioral context: zero side effects, non-joining, and a latency boundary ('Takes up to 10 seconds to report an empty network'). 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.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    Two sentences, front-loaded with the primary purpose, followed by safety and latency details. Every sentence adds unique information with no redundancy.

    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?

    With no parameters, an output schema present, rich annotations, and sibling context, the description fully covers purpose, behavior, safety, and timing. It is complete for an AI agent to select and invoke correctly.

    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?

    The tool has zero parameters, so the schema leaves nothing undocumented. The baseline for 0 params is 4, and the description correctly focuses on behavior rather than introducing non-existent 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 uses a specific verb ('List') with a clear resource ('YAAC channels') and adds scope ('currently on the air and how many participants each has'). It distinguishes itself from the sibling tool join_channel by explicitly stating it does not join anything.

    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 states it is 'safe to call at any time' and explicitly contrasts with joining ('does not join anything'), implying when to use it over join_channel. However, it does not explicitly name the alternative or provide a detailed when-to-use/when-not-to-use scenario.

    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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  • Confirm that there are no obvious security issues.
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