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Glama

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by io.github.UnityNodes

get_github_discussion

Read one Celestia GitHub Discussion (celestiaorg/docs and other Celestia repos) cached on THIS server — full body plus comments — by an id you got from list_github_discussions on this server. Celestia cache only: if the id was not returned by this servers list_github_discussions, or the request is just a raw GitHub node id (e.g. D_kw...) with no Celestia context, this is NOT the tool — use a dedicated GitHub tool for arbitrary GitHub Discussions. This is GitHub Discussions, not the community forum (use get_discussion). Pair it with list_github_discussions, which supplies the valid ids.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesGitHub GraphQL node ID, e.g. "D_kwDOMNgu5s4AY..."

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.7/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It reveals the cached-on-server behavior, the scope limitation to Celestia ids, and the fact that it returns full body plus comments. It doesn't detail error behavior for invalid ids, but the key behavioral boundaries are clearly disclosed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

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

The description is somewhat verbose but every sentence contributes: purpose, scope constraints, exclusions, and pairing advice. It is front-loaded with the main action and then clarifies boundaries. Slightly more concise phrasing would be possible, but the structure is logical and necessary.

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?

For a single-parameter read tool with no output schema, the description is complete: it states what it does, what input is valid, what to avoid, and how to obtain valid ids. It also mentions the return content (full body plus comments). No gaps remain for an 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.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema only describes id as a GraphQL node ID, but the description adds crucial semantics: the id must come from list_github_discussions, and raw GitHub node ids are invalid. This constraint is essential for correct usage and goes well beyond the schema, giving the agent necessary context.

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 a single Celestia GitHub Discussion by id, including full body and comments, with clear scope (cached on this server, Celestia repos). It explicitly distinguishes from get_discussion (forum) and generic GitHub tools, so purpose is unambiguous and well-differentiated.

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?

Provides explicit when-to-use guidance: only for ids returned by list_github_discussions on this server. Explicitly states when NOT to use (raw GitHub node ids) and directs to alternative tools (dedicated GitHub tool, get_discussion). Mentions pairing with sibling tool list_github_discussions.

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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TDQS

A3.9/5.0
Disambiguation4/5

Most tools target clearly distinct content types and actions, and descriptions carefully carve out boundaries (e.g. get_network_state vs get_network_stats, get_discussion vs get_github_discussion). However, the overlapping get_/find_/search_ families plus the very similar network_state/network_stats names leave some edge cases where an agent could select the wrong tool.

Naming Consistency4/5

The dominant convention is verb_noun (find_*, get_*, list_*, search_*), and get/list/find roughly map to id-based retrieval, browsing, and discovery. Deviations like learning_path, ecosystem_dependency_graph, and semantic_search break the pattern, and the get_ vs find_ vs search_ boundaries are not perfectly predictable.

Tool Count2/5

43 tools is on the high side for a single MCP server; even though the Celestia knowledge domain is broad, the surface is heavy and will increase selection cost. Most tools are individually useful, but the set would benefit from consolidation, e.g. merging release tools or search variants.

Completeness4/5

The server covers an unusually broad range of content types—CIPs, docs, forum, GitHub issues/discussions, releases, videos, whitepapers, ecosystem, and network state—with list/get/search access for most. Minor gaps remain, such as no dedicated blog retrieval and get_issue_status only returning status rather than full issue body, but core knowledge workflows have no dead ends.

Resources