Search blobpedia's indexed Celestia talks/videos (YouTube transcripts). Celestia-specific. Returns matches across title + transcript with a short snippet around the hit.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max rows (default 5). | |
| query | Yes | Free-text query. Appears in title or transcript. | |
| offset | No | Skip this many before returning, for paging past the limit. The response states the full count and echoes the offset used. |
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, the description carries the disclosure burden. It transparently reveals the indexed data source, that matching spans title and transcript, and that results include a short snippet around the hit. The read-only nature is implied by 'search' and 'returns', though no explicit safety or rate-limit details are provided.
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 tight sentences with no filler: it front-loads the purpose, adds domain scope, and then states the matching and snippet behavior. Every sentence 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?
For a simple 3-parameter search tool with no output schema, the description provides the essential context: what is searched, the domain scope, which fields are matched, and what the returned results look like. It does not specify full result fields or ordering, but nothing required to invoke it correctly is missing.
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 all three parameters (query, limit, offset) already have meaningful descriptions. The tool description adds little parameter-specific meaning beyond confirming title/transcript matching, which is also already in the schema, so the baseline score of 3 applies.
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 ('Search') plus a clearly defined resource ('blobpedia's indexed Celestia talks/videos (YouTube transcripts)') and states the match scope ('title + transcript') and output type ('short snippet'). This distinguishes it from generic search siblings by emphasizing its Celestia-specific video/transcript focus.
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 gives clear context by stating it is Celestia-specific and searches indexed talks/videos, which helps an agent choose it for video-content discovery over broader search tools. It does not explicitly name alternatives or state when not to use it, but the scope is clear enough for correct selection.
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.