Rank likely Celestia experts on a topic across blobpedia: forum activity matching the topic, plus overall forum volume of that author. Celestia-specific.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max authors (default 5). | |
| topic | Yes | Topic / domain to find experts for. |
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?
With no annotations provided, the description carries the full burden of disclosing behavior. It does explain that ranking is based on forum activity matching the topic plus overall author volume, which is useful. However, it doesn't mention output format, potential empty results, or any limitations of the ranking heuristic.
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?
The description is two sentences with zero filler. The primary action and resource are front-loaded, and the ranking criteria are stated compactly. Every word adds value.
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?
For a two-parameter tool, the description is mostly complete for invoking it: topic name and optional limit are clear. But because there is no output schema, the description should explain what the ranked result looks like (e.g., author names, scores, counts); it doesn't. This is a notable gap, though not critical for basic invocation.
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%, so the schema already documents both parameters (limit and topic). The description adds no new parameter-specific details beyond restating that the topic drives forum matching, which is already implied by the parameter name and schema description.
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 states a specific verb ('Rank') and a specific resource ('likely Celestia experts on a topic across blobpedia'). It also reveals the ranking methodology (forum activity matching the topic plus overall forum volume), which makes its purpose distinct from sibling tools like search, find_similar_projects, or find_maintainer_guidance.
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 clearly establishes when to use this tool: when the user wants to identify experts on a topic within the Celestia ecosystem via blobpedia forum activity. It doesn't explicitly name alternatives or exclusion conditions, but the domain-specific phrasing ('Celestia-specific') and expert-ranking focus provide enough contextual signal to route an agent.
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.