Surface forum posts authored by top-volume Celestia forum contributors (likely maintainers/core team) on a topic. Celestia-specific. Use when you want to weight expert voices over the general forum.
| Name | Required | Description | Default |
|---|---|---|---|
| topic | Yes | Topic keyword, e.g. "namespace", "light node sync", "blob submission". |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full disclosure burden. It discloses the key selection behavior (top-volume contributors, likely maintainers/core team, Celestia-specific) and the intent of weighting expert voices. However, it does not explain how 'top-volume' is determined, what the return format is, or any limitations, leaving some behavioral ambiguity.
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 filler. The first sentence states the core action and resource; the second sentence gives a clear usage directive. Every part earns its place.
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?
The tool is simple: one parameter, no output schema, no nested objects. The description explains what it does, its scope, and when to use it. It could mention return shape or caveats, but for selecting and invoking this tool the description is largely sufficient.
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 description coverage is 100% and the single 'topic' parameter is already well documented with an example. The tool description adds only the generic phrase 'on a topic,' which adds little beyond the schema. Baseline 3 is appropriate because the schema handles parameter semantics.
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 uses a specific verb ('Surface') with a specific resource ('forum posts authored by top-volume Celestia forum contributors') and scopes it by topic. It distinguishes the tool from generic search by emphasizing the maintainer/core-team signal, so an agent can tell it apart from siblings like search or semantic_search.
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 explicitly states when to use it: 'Use when you want to weight expert voices over the general forum.' This clearly implies the alternative is general forum search, though it does not name a specific sibling tool or provide explicit when-not-to-use conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.