Construct a structured Celestia onboarding sequence for a stated goal: whitepaper → relevant docs → CIPs to know → forum starter threads → talks. Celestia-specific. Topic-driven (distinct from get_started_guide, which is background-driven).
| Name | Required | Description | Default |
|---|---|---|---|
| goal | Yes | What the learner wants to do, e.g. "run a light node", "post a blob", "understand DAS". |
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?
Annotations are absent, so the description carries the full burden. It does explain what the tool produces (a structured sequence of specific resource types) and that it is Celestia-specific, but it doesn't disclose the output format, whether it returns live or static data, or any side effects. For a non-destructive construction tool, this is adequate but not rich.
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?
Three short sentences, zero filler. The main purpose and sequence are front-loaded, and the sibling distinction is placed at the end. Every sentence contributes.
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 tool with one parameter, no annotations, and no output schema, the description is close to complete: it states the goal input, the four-phase sequence, and the Celestia-specific scope. It doesn't describe the exact return representation, which would be useful, but nothing an agent needs 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?
The schema already describes the sole parameter 'goal' with examples, and coverage is 100%. The description adds only the phrase 'stated goal' plus the topic-driven framing, which does not materially extend what an agent already sees in the schema. Baseline 3 is appropriate.
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 opens with a specific verb ('Construct') and a precise resource ('structured Celestia onboarding sequence') for a stated goal, and enumerates the exact components (whitepaper, docs, CIPs, forum threads, talks). It also distinguishes itself from the sibling get_started_guide, so an agent can tell the tools apart without inspecting schemas.
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?
It explicitly contrasts this tool with get_started_guide: 'Topic-driven (distinct from get_started_guide, which is background-driven).' This states the condition for choosing this tool over the primary alternative, which is explicit when/when-not guidance.
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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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.