blobpedia

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

No annotations provided, so description carries burden. Describes read operation but lacks details on side effects, authorization, or output format. Adequate but not comprehensive.

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 efficient sentences, each adding value. No fluff, clearly structured.

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?

Simple read tool with one parameter and no output schema. Covers purpose, scope, and usage. Missing output format description, but overall adequate.

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 covers id parameter with detailed description (100% coverage). Description adds no additional parameter info beyond schema, meeting baseline.

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?

Description clearly states 'Read the full text of one Celestia Improvement Proposal (CIP) by its id.' Identifies specific verb, resource, and scope, and distinguishes from siblings like get_cip_attachments and get_cip_history.

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?

Explicitly states when-not to use ('not GitHub issues or arbitrary proposals') and provides alternative ('use a GitHub tool for those'). Also advises to get ID from search or list_cips first.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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 correctly indicates a read operation ('List') with no destructive behavior, and specifies the governance scope. It does not discuss edge cases (e.g., invalid id), but for a simple list tool this is acceptable.

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, no unnecessary words. The first sentence states the core action and scope, the second provides context for the parameter. Ideal front-loading.

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 simple parameter and no output schema, the description fully covers what the tool does, what it returns, and how to obtain the input. No gaps remain.

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 coverage is 100% with one parameter id described with example values. The description adds value by explaining the parameter as 'CIP id' and directing the agent to obtain it from search or list_cips, which goes beyond the schema's static 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 clearly identifies the tool's action (list), resource (file/PDF attachments of a CIP), and scope (Celestia governance only). Examples of attachment types (signed vote records, supporting docs) further clarify the purpose. It is distinct from siblings like get_cip which retrieves the CIP itself.

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 tells the agent where to obtain the required id ('Get the id from search or list_cips'), providing clear context for usage. It does not explicitly state when not to use the tool or list alternatives, but the scope ('Celestia governance only') implicitly limits usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations provided; description carries full burden. States it shows timeline (read-only) and is limited to Celestia governance. Lacks details on permissions or rate limits but acceptable for a read tool.

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, front-loaded with main action, no unnecessary words. Efficient.

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?

Given single parameter and no output schema, description fully explains what it does, what it returns, and how to get input. Complete.

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 has 100% coverage with detailed id description. Tool description adds no new info beyond schema, only rephrases. 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?

Clear verb 'Show' and specific resource 'status-change timeline of one Celestia Improvement Proposal (CIP)'. Distinct from siblings like get_cip (full details) and list_cips (list all).

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?

Explicitly states requirement for a CIP id and suggests sources (search or list_cips). Does not explicitly state when not to use, but context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations provided. Description mentions searches across specific sources but lacks details on return format, permissions, or side effects. Returns are not described structurally.

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 concise sentences, front-loaded with purpose, no redundant information.

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?

No output schema; description only vaguely hints at results ('see who is discussing'). Lacks explicit return structure. Otherwise adequate for a single-param search tool.

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 covers 100% of parameter id with format description. Description adds that id comes from other tools, but no new semantics beyond schema.

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?

Clearly states verb ('find where... referenced') and resource ('CIP'), specifies exact sources (forum, blog, GitHub, sibling CIPs). Distinguishes from siblings like get_cip and list_cips.

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?

Explicitly states use case ('see who is discussing a CIP and where') and prerequisite ('Needs a CIP id from search or list_cips'). Could mention when not to use, but sufficient.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations provided, so description carries full burden. It indicates it's a read operation ('Read one... thread') and includes posts, but does not disclose pagination, rate limits, or behavior on missing threads. Adequate but lacks depth.

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 sentences, front-loaded with purpose, no wasted words. Structured logically: what it does, which channel, how to get id.

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?

Given the simple tool (1 param, no output schema), the description is complete: explains operation, distinguishes from siblings, and gives id source. No gaps.

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 coverage is 100% with a single parameter described as 'Forum topic ID'. Description adds 'by numeric topic id' but provides minimal extra value beyond schema. Baseline 3.

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?

Clearly states 'Read one Celestia community forum thread with its posts' with a specific verb and resource. Distinguishes from siblings like get_github_discussion and mentions the source (forum.celestia.org).

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?

Explicitly says this is for the CELESTIA FORUM channel, not GitHub Discussions or mailing list. Also tells how to get the id (from search or get_cip_mentions). Provides clear when-to-use and alternative tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries full burden. It discloses the read-only behavior and explains that search snippets truncate, but does not mention potential rate limits or error cases. However, for a simple read operation, this is generally sufficient.

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?

Extremely concise with only two sentences, front-loaded with the primary action, and no wasted words.

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?

Given the tool's simplicity (one parameter, no output schema), the description is complete: it covers purpose, usage context, parameter source, and exclusions. No missing information needed for correct selection and 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 coverage is 100% for the single parameter 'id'. The description adds value beyond schema by explaining how to obtain the ID (from search or semantic_search), which is critical for correct usage.

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 clearly identifies the tool's action as reading the full text of a Celestia documentation page by ID, specifying the resource and distinguishing it from sibling tools by noting its scope (Celestia docs only, not other projects or web).

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?

Explicitly states when to use (Celestia operational/how-to questions), when not to use (other projects' docs or web), and provides alternatives (that projects docs tool, web-search tool). Also instructs to get the ID from search or semantic_search first.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Discloses the content (names, roles, background) and limitations (no grants, board seats). Without annotations, it carries transparency well but could mention data freshness or update frequency.

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?

Every sentence serves a purpose: first sentence defines scope, second gives examples, third clarifies exclusions. No redundancy.

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?

Given the moderate complexity (2 params, no output schema) and sibling tools, the description fully covers what data is returned and what is excluded. Users can confidently select and invoke this tool.

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 coverage is 50%; the description adds topic examples (e.g., 'team', 'executive director') but does not describe the limit parameter. Moderate compensation.

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 clearly states that the tool returns leadership and team members of Celestia Foundation and Labs, plus headline figures. It distinguishes from siblings by explicitly listing what it is NOT (e.g., not docs, not CIPs).

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?

Provides explicit use cases ('who is the CEO/CTO', 'who works on Celestia') and explicitly states when not to use it, directing to alternative tools like get_doc, list_cips, etc.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations provided, so description carries full burden. It reveals it's a read operation on cached data, returning full body and comments. Implicitly non-destructive but clear enough.

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?

Single paragraph, front-loaded with purpose, then restrictions and alternatives. Every sentence adds value, no wasted words.

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?

Given no output schema, description explains return type (full body plus comments). Explains caching and id origin. Complete for tool usage with sibling list tool.

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 has one parameter with description. Description adds context: the id must come from list_github_discussions, and raw ids without Celestia context are not valid. Adds significant value beyond schema.

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 clearly states the tool reads a specific Celestia GitHub Discussion cached on this server, returning full body plus comments. It distinguishes from sibling tools like get_discussion and dedicated GitHub tools.

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?

Explicitly states when to use (id from list_github_discussions) and when not to (raw node id without Celestia context, use dedicated GitHub tool). Provides pairing advice with list_github_discussions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations provided, but the description fully discloses behavior: data fetched in real time from Celenium indexer and CoinGecko, read-only, no side effects. The tool has zero parameters, and the description accurately conveys its live nature.

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 a single, well-structured paragraph that efficiently conveys purpose, source, data items, and usage guidance. Every sentence adds value, with no wasted words.

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?

Given the absence of an output schema, the description provides a comprehensive list of returned data (block height, supply, validator count, etc.), fulfilling the need for contextual completeness. The tool is simple and fully described.

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?

No parameters exist, and the description notes 'Zero arguments,' which aligns with the schema. Schema description coverage is 100%, so baseline is 4. The description adds no confusion.

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 clearly states it retrieves live Celestia on-chain state and TIA market data, listing specific data points. It distinguishes from sibling tool get_network_stats by noting that get_network_stats counts indexed content, not chain state.

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?

Explicitly states when to use: 'for current/live chain questions or TIA price right now.' Provides a clear contrast with get_network_stats, helping the agent choose between them.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations are provided, so the description carries full burden. It explicitly states the data is catalog coverage, not live state, which is the key behavioral trait. However, it does not mention potential limitations like update frequency or whether data is cached.

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, immediately states the purpose, and uses capitalization to highlight key distinctions. Every sentence adds value with no redundancy.

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?

Given the tool's simplicity (no parameters, no output schema), the description fully explains what it returns and how it differs from the sibling tool. No additional context is needed.

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?

There are zero parameters, and the description mentions 'Zero arguments.' Since schema coverage is 100% (no parameters), the description adds no parameter-specific meaning, but the baseline for zero parameters is 4.

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 clearly states it provides 'Counts of CELESTIA CONTENT indexed by blobpedia', specifying the resource and action. It explicitly distinguishes from the sibling tool get_network_state, which covers live chain metrics, making the purpose unmistakable.

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 explicit guidance on when NOT to use this tool: for live chain metrics or market data, use get_network_state. It also notes that the tool requires zero arguments, simplifying selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations are provided, so the description must disclose behavioral traits. It reveals the tool is read-only (timeline), newest-first ordering, and Celestia-specific scope. It does not mention auth or rate limits, but these are not critical for a simple timeline tool. Overall adequate transparency.

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, very concise, and front-loads the main purpose. Every sentence is informative and non-redundant. No wasted words.

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?

Given no output schema, the description could specify return format or item structure. However, it lists the types of changes covered, which is sufficient for a timeline tool. The context is complete enough for an agent to understand the scope.

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 coverage is 0%, yet the description adds no information about the two parameters ('days' and 'limit'). It does not explain their purpose, defaults, or effects. The parameters remain unelaborated, so the description fails to add meaning beyond the schema.

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 clearly states the tool provides a timeline of recent changes related to Celestia, covering specific sources (CIP status, proposals, forum, blog, GitHub releases). It specifies 'newest first' and distinguishes it from a GitHub commit feed or general crypto news, making the purpose unambiguous.

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 tells when not to use this tool: 'not a GitHub commit feed (use a GitHub tool) and not general crypto news (use a web-search tool).' It also directs to 'get_trending for what is popular rather than what is new', providing clear alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries full burden. It discloses that the tool will 'ASK the user about their background FIRST' and then 'return a path with docs.celestia.org links'. This reveals the interactive behavior and output format. Could be clearer on what happens if background is not one of the enum values, but schema covers that.

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. The first sentence is concise and front-loaded with purpose. The second sentence packs usage guidelines, exclusions, and procedure, making it a bit long. Still, every sentence provides necessary information.

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?

Given the tool's simplicity (single enum parameter, no output schema, no annotations), the description covers purpose, usage, parameter semantics, and exclusions. It doesn't specify if the tool can be reused or if it's stateless, but those are less critical for a guide tool. Overall, it's sufficiently complete for an agent to invoke correctly.

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 coverage is 100% with one parameter 'background'. The description adds that the tool will ask the user to pick one before proceeding, which adds interactive context beyond the schema's enum description. It also implies the parameter determines the output path, adding semantic value.

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 clearly states the tool provides 'personalized onboarding for building ON CELESTIA' and lists specific use cases (running a node, posting a blob, deploying a rollup). It distinguishes itself from siblings like 'search' by saying 'prefer it over the Celestia search tool'.

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?

Explicitly states when to use: 'ALWAYS use this when a developer asks how to get started or build ON CELESTIA'. Also specifies when not to use: 'NOT for getting started with non-Celestia frameworks...' and mentions alternative tools (general docs or web-search tool). Includes step to ask about background first.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations are provided, so the description must carry the behavioral disclosure burden. It explains what the tool returns (trending Celestia content) and what it does not return (price data, general trends). While it doesn't describe the return format or pagination, the simplicity of a zero-argument tool makes this acceptable. The negation clauses add valuable context.

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 front-loaded with the main purpose and maintains good structure. It uses negation for clarity and explicitly states zero arguments. It could be slightly more concise, but the added context justifies its length.

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?

Given zero parameters, no output schema, and a simple use case, the description is complete. It covers what the tool does, what it excludes, and when to use it. No additional context is needed for an agent to select and invoke this tool correctly.

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 tool has zero parameters, so the baseline is 4. The description adds no parameter information, but none is needed. The high schema coverage (100%) supports the baseline.

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 clearly states the tool shows trending Celestia content (CIPs, forum threads, docs) on blobpedia. It explicitly distinguishes itself from price/market trends and general web trends, and differentiates from sibling tools that fetch specific items rather than trending content.

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 provides explicit when-to-use guidance: only when the user asks what is popular within Celestia content. It gives clear when-not-to-use scenarios, directing to CoinGecko for price/market trends and to a web-search MCP for general trends.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations provided, so description carries full burden. It discloses key behavioral traits: cached on server, Celestia-only, returns metadata plus transcript. Could be improved by mentioning that it does not modify state and that the cache may be stale, but current info is solid.

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, no wasted words. Front-loaded with core action and scope, then caveats and alternative usage. Every sentence serves a purpose.

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?

Given no output schema, description mentions return type (metadata plus transcript), which is sufficient for a get operation. Could elaborate on metadata fields, but not required for selection and invocation. Overall adequate for the tool's simplicity.

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?

Input schema has 100% coverage with a description and example. The tool description adds context by explaining that the ID is a YouTube video ID with an example, which reinforces the schema's documentation. Baseline 3 is appropriate as schema already provides sufficient meaning.

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 specific verbs ('Get', 'cached') and resource ('one Celestia-related YouTube video') with clear scope. It distinguishes from siblings by stating it does not fetch arbitrary YouTube videos or search YouTube at large, and directs users to a dedicated YouTube tool for that.

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?

Explicitly states when to use (get a cached Celestia video) and when not to (for arbitrary YouTube videos or search), naming the alternative (dedicated YouTube tool). Also tells how to find IDs via list_videos or search.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Without annotations, the description implies a read-only operation via 'Read', which is clear. It warns about slug validity but could explicitly state no side effects or rate limits. Still, it provides good behavioral context.

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 efficient sentences: first states purpose, second adds constraints and prerequisite. No wasted words, well-front-loaded.

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?

Covers purpose, scope, and prerequisite. Lacks explicit output format description, but 'Read the full text' reasonably implies the output. Adequate for a simple one-parameter tool.

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 coverage is 100%, and the description adds value by explaining the slug's role and directing to list_whitepapers for valid values, giving real-world context beyond the schema.

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 clearly states it reads the full text of one Celestia whitepaper by slug, distinguishing it from siblings like get_cip and from arbitrary web PDFs via explicit exclusion.

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?

Explicitly states when to use (Celestia papers only), when not to use (use web-search for arbitrary PDFs), and provides a prerequisite: call list_whitepapers first to get a valid slug.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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 states the tool is for browsing/filtering CIPs, which implies read-only, but lacks details on pagination, ordering, data freshness, or any other behavioral traits. Adequate but not comprehensive.

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, front-loaded with purpose. Every sentence adds value with no redundancy. The negative usage guidance is concise and helpful.

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?

There is no output schema, so the description should explain the return format. It only says 'browse or filter' without indicating what fields or structure the agent can expect. This is a significant gap for a list tool.

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 coverage is low (33%), so the description should compensate. It mentions filtering by status (listing Draft, Review, Final) and type, but does not explain the limit parameter or other statuses. It adds some value beyond the schema but is incomplete.

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 clearly states the action ('Browse or filter') and resource ('Celestia Improvement Proposals (CIPs)') and distinguishes the tool from siblings by explicitly naming get_cip, get_cip_history, and get_cip_mentions as alternatives for other operations.

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 tells when not to use the tool (listing GitHub issues, PRs, or proposals from other chains) and directs to alternatives (GitHub MCP or that chain's tool). It also guides to siblings for other use cases.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations exist, so the description fully carries the behavioral burden. It discloses that the tool returns catalog entries, not live project data, which is a key behavioral trait. However, it could mention additional details like result ordering or whether pagination is needed.

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, front-loads the core purpose, and every sentence provides essential information without redundancy.

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?

With 3 parameters, no annotations, and no output schema, the description is fairly complete: it defines scope, return type, and filtering options. It could be more complete by noting whether the catalog is static or dynamic, but it adequately covers the essentials.

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 coverage is 67% (query and category have descriptions; limit has only schema constraints). The description adds value by mentioning 'filtered by category or keyword,' which clarifies the purpose of query and category. The limit parameter is not explicitly described but its schema provides min/max/default, so the overall semantic contribution is good.

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 clearly states the tool browses the Celestia ecosystem catalog, lists specific project types (rollups, DeFi, wallets, infrastructure), and distinguishes itself from a general crypto-project directory by suggesting an alternative (web-search tool).

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 tells when not to use this tool ('Celestia ecosystem only — not a general crypto-project directory') and directs the agent to a web-search tool for broader queries, providing clear usage boundaries.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description discloses caching, ranking, and scope limitations. It doesn't cover error handling or data freshness, but these are secondary for a read-only list operation.

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 succinct sentences: purpose and ranking, scope and alternative, and differentiation from two siblings. No wasted words.

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?

Covers what, from where, ranking, and when to use alternatives. Lacks return format description, but the tool is straightforward and likely returns standard discussion objects.

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 description provides example values for repo and category, and explains the context (ranking by upvotes) that gives meaning to limit. This adds value beyond the schema's 67% coverage, though limit's role is implied rather than explicit.

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 clearly states the tool lists GitHub Discussions from Celestia repos, cached and ranked by upvotes then recency. It distinguishes itself from sibling tools for arbitrary GitHub Discussions, full thread details, and the community forum.

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?

Explicitly guides when to use this tool (Celestia repos only, cached) and when to use alternatives: get_github_discussion for full body/comments, get_discussion for community forum, and a dedicated GitHub tool for non-Celestia discussions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Despite no annotations, description declares read-only behavior, zero arguments, and limited scope. Fully transparent about what the tool does and does not do.

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, no waste. Purpose stated first, then clarification and instructions. Efficient and well-structured.

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, parameter-free tool with no output schema, the description covers purpose, scope, limitations, and usage guidance completely.

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?

No parameters; description states 'Zero arguments' which aligns with schema. Baseline score of 4 for 0-param tools 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?

Clearly states verb (List), specific resource (GitHub repositories indexed for Celestia), and scope (blobpedia's index, not user's account). Distinguishes from general GitHub tools.

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?

Explicitly says when to use (view Celestia index) and when not to (for user's own repos), and suggests using a dedicated GitHub tool. Provides clear context and alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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 discloses key traits: the data is cached (not live), limited to Celestia, metadata-only, and supports filtering. It could mention data freshness or pagination but is adequate for a simple list tool.

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 three concise sentences, each adding unique value: purpose+filters, limitation+alternative, and distinction from sibling. No unnecessary words, front-loaded with key information.

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?

Given the simple tool with 5 optional parameters and no output schema, the description covers purpose, scope, limitations, and relationship to siblings. It could specify the return metadata fields but is otherwise complete for list functionality.

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 description adds context beyond the input schema by stating the filters (channel/date/transcript) and the cache scope, which helps interpret parameters. Schema covers 60% of parameters with descriptions; the description reinforces and contextualizes them without duplicating.

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 clearly states the tool browses Celestia-related cached YouTube videos and differentiates from get_video (transcript retrieval) and general YouTube search. It specifies the resource (cached videos) and action (browse/filter) explicitly.

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 provides explicit guidance: use this for browsing cached Celestia videos, avoid for general YouTube search (directs to dedicated tool), and suggests get_video for transcripts. This helps in selecting the right tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations provided, so description carries full burden. It discloses that it lists indexed content and returns slug plus title. No mention of destructive actions or auth needs, which is appropriate for a read-only list tool. Could add pagination details, but not critical with 0 parameters.

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 concise sentences, each earning its place: first states purpose and output, second sets scope, third links to sibling tool. No fluff.

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?

Description is sufficient for a simple list tool with no parameters. No output schema, but it specifies return fields (slug plus title). Could mention ordering but not necessary given sibling tools cover other purposes.

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?

No parameters; schema coverage is 100% (empty). Baseline 4 is appropriate since description doesn't need to add parameter info and does not repeat schema.

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 clearly that it lists Celestia whitepapers and research PDFs with slug and title. It distinguishes itself from arbitrary web PDFs by specifying 'Celestia-specific' and contrasts with web-search tools.

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?

Explicitly says when to use (for Celestia whitepapers) and when not (for arbitrary web PDFs, use a web-search tool). Also directs to use get_whitepaper for reading a specific one, providing clear alternative.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Description discloses scope and basic search behavior, but omits details about result format, pagination, or error handling. No annotations, so description carries full burden; missing some behavioral context.

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, front-loaded with purpose. Second sentence lists exclusions/alternatives efficiently. Minor verbosity but overall concise and well structured.

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?

Given no output schema and 3 parameters, description adequately covers usage boundaries and differentiates from siblings. However, lacks explicit mention of output format or behavior in edge cases (e.g., empty results).

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 coverage is only 33% (query described). Description does not explain 'type' enum values or 'limit' parameter. With low coverage, description should compensate but does not.

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?

Description specifies exact verb ('search'), resource ('Celestia knowledge base'), and enumerates covered sources (CIPs, docs, etc.). Clearly distinguishes from siblings like semantic_search and get_doc.

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?

Explicitly states when to use (exact-term/name lookups), when not to use (other blockchains, general web, etc.), and provides alternatives (semantic_search for conceptual, get_doc for full page).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations are provided, so the description carries full burden. It discloses the semantic nature and scope, but lacks details on rate limits, data freshness, error handling, or pagination. 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?

The description is two sentences, front-loaded with purpose, and every phrase earns its place. No redundancy or verbosity.

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?

Given no output schema and 3 params with moderate schema coverage, the description adequately covers scope, exclusions, and usage. Lacks return format details, but for a search tool, it is fairly complete.

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 coverage is 67% (2 of 3 params have descriptions). Description adds context for query as 'semantic meaning-based' and lists source types inline, but does not elaborate on limit parameter beyond schema defaults. Baseline 3, modest added value.

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 clearly states the tool performs semantic meaning-based search over the Celestia knowledge base, listing specific content types (CIPs, docs, etc.) and explicitly differentiating from sibling tools like search and get_doc.

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 provides explicit guidance on when to use (conceptual questions) and when not to use (other blockchains, web, local files), and names alternative tools: search for exact-term lookups and get_doc for full page reads.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.