blobpedia

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

No annotations provided, so description carries burden. Indicates read-only operation ('Read'), and clarifies scope. Does not detail additional behaviors like auth or rate limits, but sufficient for a simple retrieval 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, no redundancy, front-loaded with primary action. Every sentence adds essential 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?

Tool is simple (one param, read-only, no output schema). Description fully covers purpose, scope, parameter source, and exclusions. Complete for an AI agent to use effectively.

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 parameter fully (100% coverage), so baseline is 3. Description adds value by referencing prerequisite tools for obtaining the ID ('Get the id from search or list_cips first').

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 verb 'Read' and resource 'full text of one CIP by its id'. Distinguishes from siblings by specifying 'Celestia governance docs only — not GitHub issues or arbitrary proposals'.

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 (CIP governance docs) and when not to (use GitHub tool for others). Provides guidance 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 implies a read-only list operation but does not explicitly state non-destructiveness or lack of side effects. The examples add context about attachment types, but behavioral traits like authorization requirements or rate limits are not mentioned.

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, 30 words, with zero wasted content. Each sentence serves a purpose: first defines the action and examples, second adds scope and parameter source.

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 list tool with one parameter and no output schema, the description covers purpose, scope, parameter acquisition, and examples. It is slightly incomplete in not describing the return format or listing fields, but this is acceptable given the straightforward nature.

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 has 100% coverage for the single parameter, providing format examples. The description adds value by telling the agent to obtain the id from sibling tools ('search or list_cips'), which is actionable guidance beyond the schema's scope.

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 verb 'list' and the resource 'attachments of one Celestia Improvement Proposal (CIP)', with specific examples like 'signed vote records, supporting docs'. It distinguishes from siblings such as get_cip and list_cips by focusing on attachments and specifying governance scope.

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 context: 'Celestia governance only' and 'Get the id from search or list_cips', guiding the agent on when to use and how to obtain the required parameter. However, it does not explicitly state when not to use or list alternative tools for other purposes.

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 behavioral transparency burden. It correctly indicates a read-only operation ('Show the status-change timeline'), but does not disclose potential authentication requirements, rate limits, or any other behavioral traits beyond the basic action.

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 the action, and contains no fluff. Every sentence adds 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?

The tool has no output schema, but the description explains the output type (status-change timeline with examples). For a simple, single-parameter tool, this is sufficient. It does not mention pagination or limits, but that is acceptable given 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 description coverage is 100% for the only parameter (id), so baseline is 3. The description adds marginal value by suggesting the id comes from search or list_cips, but the schema already describes the id format. The description does not significantly enhance parameter understanding 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 shows the status-change timeline of a CIP, with specific examples of transitions (Draft to Review to Final). This distinguishes it from siblings like get_cip (which likely shows overall CIP details) and list_cips (which lists 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?

The description specifies that the tool is for Celestia governance only and requires a CIP ID obtained from search or list_cips. This gives clear context on when to use it, though it does not explicitly state when not to use it or mention 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 provided, so description carries full burden. States what it does and required input, but does not disclose traits like rate limits, authentication needs, or behavior when no mentions exist. Acceptable for a simple lookup 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: first lists sources, second adds context and prerequisite. Efficient with minimal redundancy, though the first sentence is slightly long.

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 single-parameter tool with no output schema, description adequately explains purpose, input, and sources. No mention of return format, but that is expected given no output schema.

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 describing the 'id' parameter format. Description adds value by explaining where to obtain the id (from search or list_cips) and referencing it in context.

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?

Specifies exact verb 'find' and resource 'references' across multiple sources. Clearly distinguishes from siblings like get_cip (retrieve specific CIP) and get_discussion (retrieve a specific discussion).

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 'Celestia-specific cross-reference — use to see who is discussing a CIP and where.' Mentions prerequisite (CIP id from search or list_cips). Does not explicitly state when not to use, but sibling list provides context.

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 burden of disclosing behavior. It clearly states this is a read operation ('Read') and specifies the scope ('a thread with its posts'). It also clarifies the source channel (forum.celestia.org vs GitHub Discussions). It doesn't mention error conditions or response format, but for a simple read-only tool, the behavior is sufficiently transparent.

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 just two sentences, with the core purpose in the first sentence and usage guidance in the second. Every word earns its place, 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 low complexity (single parameter, no output schema), the description covers the essential context: what it reads, where to get the id, and how to avoid confusing it with a sibling tool. It doesn't detail the return structure, but 'with its posts' implies that, and it's sufficient for a straightforward read operation.

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 provides 100% coverage for the single parameter 'id' (type integer, description 'Forum topic ID'). The description adds value by noting the id is numeric and providing concrete sources for obtaining it ('from search or get_cip_mentions'), which goes 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's function: 'Read one Celestia community forum thread (forum.celestia.org) with its posts, by numeric topic id.' It uses a specific verb ('Read'), identifies the resource (Celestia forum thread), and distinguishes it from the sibling tool get_github_discussion.

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 the agent when to use this tool versus an alternative: 'This is the CELESTIA FORUM channel — not GitHub Discussions (use get_github_discussion).' It also provides guidance on how to obtain the required id: 'Get the id from search or get_cip_mentions.'

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. It describes the tool as reading full text, implying read-only behavior. Does not explicitly state no side effects, but the description is sufficient for a read 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?

Description is three sentences, front-loaded with core functionality. No unnecessary words, every sentence 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?

Tool is simple with one parameter and no output schema. Description explains what output contains (full commands, flags, sections) but doesn't specify return format explicitly. Adequate for a read-only 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, coverage 100%. Description adds context that id comes from search/semantic_search, but does not add new semantic information beyond schema. Baseline 3 for high coverage.

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 it reads full text of a Celestia documentation page by id. It specifies the resource (Celestia docs) and distinguishes from siblings like search/semantic_search (which return snippets) and other docs 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?

Provides explicit when-to-use (Celestia operational/how-to questions) and when-not-to-use (not for other project docs, not for web). Advises prerequisite: get 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?

No annotations are provided, so the description carries the burden of behavioral disclosure. It does not mention side effects, authorization, or data freshness, but it does clarify the tool's scope (people and org facts only), which adds some transparency. For a simple read-only tool, this is 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?

The description is a single paragraph that efficiently covers purpose, examples, and exclusions. It is well-structured and informative without being verbose. Minor improvement could be to separate usage guidelines more clearly.

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 output schema, and moderate schema coverage, the description provides a good overview of what is included and excluded. However, it does not describe the output format, which would help an agent interpret results. Overall, it is largely complete for decision-making.

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% (only 'topic' has a description; 'limit' lacks one). The description does not elaborate on parameters or their usage beyond the schema. For a tool with two parameters and low schema coverage, the description should compensate but does not, making parameter understanding reliant solely on 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 info on who runs Celestia, including leadership, team members, roles, background, and headline figures. It explicitly distinguishes itself from other tools by listing what it does NOT cover (documentation, CIPs, etc.), which is strong differentiation.

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 usage examples (e.g., 'who is the CEO/CTO') and explicit when-not-to-use conditions with references to alternative tools (e.g., use search or get_doc for documentation). This fully satisfies the guideline dimension.

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 reveals the cached-on-server behavior, the scope limitation to Celestia ids, and the fact that it returns full body plus comments. It doesn't detail error behavior for invalid ids, but the key behavioral boundaries are clearly disclosed.

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 somewhat verbose but every sentence contributes: purpose, scope constraints, exclusions, and pairing advice. It is front-loaded with the main action and then clarifies boundaries. Slightly more concise phrasing would be possible, but the structure is logical and necessary.

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 single-parameter read tool with no output schema, the description is complete: it states what it does, what input is valid, what to avoid, and how to obtain valid ids. It also mentions the return content (full body plus comments). No gaps remain for an agent to select and 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?

The schema only describes id as a GraphQL node ID, but the description adds crucial semantics: the id must come from list_github_discussions, and raw GitHub node ids are invalid. This constraint is essential for correct usage and goes well beyond the schema, giving the agent necessary context.

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 single Celestia GitHub Discussion by id, including full body and comments, with clear scope (cached on this server, Celestia repos). It explicitly distinguishes from get_discussion (forum) and generic GitHub tools, so purpose is unambiguous and well-differentiated.

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 when-to-use guidance: only for ids returned by list_github_discussions on this server. Explicitly states when NOT to use (raw GitHub node ids) and directs to alternative tools (dedicated GitHub tool, get_discussion). Mentions pairing with sibling tool 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?

Discloses real-time fetching from Celenium indexer and CoinGecko, specifies mainnet only, and lists all returned fields. No annotations provided, so description carries full burden; some missing details like latency or error handling.

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?

Front-loaded with key purpose and sources. The list of fields is dense but necessary for completeness. Could be slightly shorter but not overly verbose.

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 parameters and no output schema, the description fully covers all returned data: block height, supply, validators, blob data, transactions, accounts, namespaces, TIA price, market cap, and 24h change. 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?

Description confirms zero arguments and explains what fixed data is returned. With no parameters, baseline is 4; description adds value by listing all fields.

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 fetches live Celestia on-chain state and TIA market data in real time, distinguishing it from the sibling tool get_network_stats which counts indexed 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?

Explicitly says use this for current/live chain questions or TIA price right now, and contrasts with get_network_stats. Does not explicitly state when not to use, but the 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?

Discloses that data is index/catalog coverage, not live chain state, which is critical for correct interpretation. With no annotations, description carries full burden and does well, though it could mention potential error scenarios or return format.

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?

Description is very concise (two sentences) and front-loads the purpose, then adds distinction and alternative usage. Every sentence 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?

Given zero parameters and no output schema, the description fully explains what the tool returns and what it does not, including a pointer to the sibling tool for related functionality. 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?

Zero parameters; description confirms 'Zero arguments.' Schema coverage is 100%, so no additional param details needed. Baseline 4 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 the tool returns counts of Celestia content indexed by blobpedia (CIPs, forum threads, docs, videos, etc.) and explicitly distinguishes from live chain state. This specificity differentiates it from sibling 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?

Provides explicit guidance on when to use this tool vs. alternative: 'For LIVE on-chain metrics... use get_network_state on THIS server instead.' Also notes zero arguments.

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 states 'newest first' but lacks details on pagination, output format, or potential errors. For a simple timeline tool this is adequate but not fully transparent.

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 sentences, each adding value: purpose, boundaries, alternative. It is front-loaded and concise 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?

Despite no output schema or annotations, the description covers scope, exclusions, and alternatives. It lacks return format details, but for a simple list tool with two numeric parameters, it is nearly 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 description coverage is 0%, yet the description does not explain what 'days' and 'limit' parameters do beyond the schema's defaults and bounds. The description adds no parameter-specific 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 clearly states the tool retrieves a timeline of Celestia changes across specific sources (CIP status, proposals, forum, blog, GitHub releases) and explicitly distinguishes it from sibling tools like GitHub commit feeds or general crypto news.

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 (Celestia activity) and when-not-to-use (GitHub commits, general news) and directs to get_trending for popularity, offering 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?

No annotations are provided, so the description carries the full burden. It discloses the need to ask the user's background and return a path with documentation links. However, it does not specify the exact format of the response (e.g., whether it returns a list of links or a curated message) or any behavioral constraints like rate limits or required permissions. This is a minor gap given the tool's simplicity.

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 sentences long and front-loaded with the tool's purpose. Every sentence adds value: purpose, usage guidelines, and parameter instructions. While it is slightly verbose, the information density is high and no words are wasted.

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, no annotations), the description is complete. It covers the purpose, when to use, how to use (including the interaction pattern of asking background), and what to exclude. No additional information is needed for an agent to correctly 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 100% with one parameter 'background' that has enumerated values. The description adds meaning beyond the schema by explaining that the parameter represents the developer's primary goal and instructs the agent to ask the user to pick one before proceeding. This guidance is crucial for correct tool invocation.

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, with specific examples like running a node, posting a blob, or deploying a rollup. It distinguishes itself from siblings by explicitly recommending it over the Celestia search tool for getting-started questions.

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 this tool ('ALWAYS use this when a developer asks how to get started or build ON CELESTIA') and when not to ('NOT for getting started with non-Celestia frameworks'). It also provides a specific instruction to ask the user about their background first, and directs to defer to general tools for off-topic queries.

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 description clarifies it is a read operation (no arguments, returns trending content). Could mention that it does not modify any data, but the negative scope statements (NOT market trends, etc.) aid 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?

Four sentences with essential information, front-loaded with purpose. No fluff or 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?

While the purpose and usage are clear, the description does not detail the output structure (e.g., list of titles, URLs). However, for a simple trending tool without output schema, this is a minor gap.

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, so schema coverage is 100%. Description explicitly states 'Zero arguments', reinforcing the absence. Baseline of 4 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 clearly states the tool shows trending CELESTIA content on blobpedia, listing specific types (CIPs, forum threads, docs). It distinguishes from sibling tools like get_cip which target specific 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?

Explicitly states when to use (user asking about popular Celestia content) and when not (market trends, general web trends), with specific references to alternative MCPs for those 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?

With no annotations, the description discloses the tool's scope (Celestia cache only, no arbitrary YouTube), and mentions it returns metadata and transcript. It is transparent about being a read operation from cache, though does not detail error handling or rate limits.

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, each purposeful: first defines the action and result, second clarifies exclusions, third provides id source. No wasted 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?

Despite being a simple get tool, the description lacks detail on the output structure beyond 'metadata plus transcript'. With no output schema, more specificity about return fields would improve completeness for agent 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?

The schema provides 100% coverage with a description for the 'id' parameter. The description adds meaning by specifying that the id is for a Celestia video and sourcing ids from list_videos or search, enhancing understanding 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 verb 'Get' and the resource 'one Celestia-related YouTube video cached on this server', and specifies what is returned (metadata plus transcript). It explicitly distinguishes from fetching arbitrary YouTube videos, which helps differentiate from other 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?

Provides explicit when-to-use (get a cached Celestia video by id) and when-not-to-use (not for arbitrary YouTube, use dedicated YouTube tool). Also guides on how to obtain the id via list_videos or search, offering clear context for 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 provided, so description bears full burden. It states 'Read the full text', indicating a read-only operation without side effects. However, it does not specify the response format or any potential limitations like size.

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, 30 words, no filler. Front-loaded with the primary action. Every sentence provides essential 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?

For a simple tool with one parameter and no output schema, the description completely covers purpose, limitations, and prerequisite. No gaps given the complexity.

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% (one parameter 'slug' described). The description adds value beyond the schema by explaining what a slug represents and advising to get a valid slug from list_whitepapers.

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?

Describes the action ('Read the full text'), resource ('one Celestia whitepaper or research PDF'), and how to identify it ('by slug'). Clearly distinguishes from siblings like get_doc and list_whitepapers.

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 arbitrary web PDFs') and suggests alternatives ('use a web-search tool'). Also 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 provided, so description carries full burden. It clarifies the domain (Celestia governance proposals only) and implies a safe read operation. Could be more explicit about pagination or result format, but adequately conveys the tool's read-only nature and scope.

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 action and domain. Every sentence adds value: first states what it does, second gives usage boundaries and alternatives. 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?

For a tool with 3 parameters and no output schema or annotations, the description is fairly complete. It covers purpose, when to use, and alternatives. Missing details on return format, but for a listing tool with filter parameters, the description is sufficient for agent decision-making.

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 33% (only type has description). Description mentions filtering by status or type, but adds little beyond schema enum values. It does not describe the limit parameter or offer additional context for the type parameter. Adequate but does not fully compensate for low schema coverage.

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/filters Celestia Improvement Proposals by status or type. It distinguishes from siblings by explicitly excluding GitHub issues/PRs and other chains' proposals, and names related tools like get_cip, get_cip_history, get_cip_mentions.

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 tells when to use (browse/filter CIPs) and when not to use (GitHub issues, PRs, other chains). Provides clear alternative tools for reading full CIPs (get_cip), history (get_cip_history), and discussions (get_cip_mentions).

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 the burden. It discloses 'Returns catalog entries, not live project data,' implying read-only, but lacks detail on auth, rate limits, or reversibility.

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, no fluff. Every sentence 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?

No output schema, so description should cover return values. It says 'catalog entries' but not structure or fields. Missing pagination details, though limit implies some. Adequate for a simple browse 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 67% (query and category described). The description reiterates these filters but adds little beyond 'optional filtering.' The limit parameter is not mentioned but has schema details.

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 (rollups, DeFi, etc.) and explicitly distinguishes it from a general crypto directory, differentiating it from sibling tools like get_cip or 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?

Explicitly states when to use (Celestia ecosystem) and when not to (use a web-search tool for general projects), providing an 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?

Discloses data source (cached), ranking order, and scope. No annotations provided, so description carries burden; it adds context about caching and ranking though omits potential staleness or authentication needs.

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 core purpose and key constraints. No unnecessary details, every sentence 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?

Covers purpose, scope, parameters, and alternatives. Lacks output format details, but acceptable for a list tool with no output schema. Adequate for agent 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?

Adds meaning to two parameters with examples (e.g., 'celestiaorg/docs', 'Q&A'). Explains ranking context. Despite high schema coverage, description provides useful usage hints 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 it lists GitHub Discussions from Celestia repos cached locally, ranked by upvotes then recency. Distinguishes from get_github_discussion (single thread) and get_discussion (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 limits to Celestia repos and local cache, warns not for arbitrary GitHub Discussions, and directs to dedicated tools. Provides clear when-to-use 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?

States 'Read-only view' and 'Zero arguments', indicating safe, simple operation. With no annotations, this covers the basic behavioral profile, but lacks detail on output format (e.g., structure of item counts).

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 action and scope, no extraneous words. 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?

Describes scope and limitations well for a simple list tool. However, without an output schema, the description could be more complete by hinting at the return format (e.g., list of repo names with counts).

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?

Zero parameters and 100% schema coverage. The description adds value by explicitly noting 'Zero arguments', confirming no inputs needed. Baseline of 4 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?

Description clearly states the verb 'list', the resource 'GitHub repositories', and specifies the scope: 'that blobpedia has INDEXED for Celestia, with item counts'. It distinguishes from sibling tools by contrasting with browsing GitHub accounts or arbitrary repos.

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 tells when not to use ('does NOT browse your GitHub account or arbitrary repos') and provides an alternative ('use a dedicated GitHub tool for that'). Clearly defines its limited context.

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 it's a server cache, metadata-only, and doesn't search YouTube. While read-only behavior is implied, explicit 'read-only' would improve. Still, it provides enough behavioral context for safe use.

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 tightly-coupled sentences: purpose, limitations, and pointer to sibling. No fluff, front-loaded with main action, and every sentence adds value. Excellent structure.

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 given, so description should cover return value. It states 'metadata only' but doesn't specify which metadata fields are returned. With 5 parameters and no output schema, this is a notable gap. However, the description is otherwise informative within its 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 60% with descriptions for some parameters. Description adds high-level filter categories (channel, date, transcript) but doesn't detail specific parameter formats or constraints beyond what schema already provides. Adds some context but not substantial new 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 clearly states the tool lists Celestia-related YouTube videos from a cached server, with filters. It uses a specific verb 'Browse' and resource 'YouTube videos cached on this server', distinguishing it from siblings like 'get_video' and general YouTube 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 states when not to use: 'does NOT search YouTube at large' and directs to 'use a dedicated YouTube tool for that'. Also points to 'get_video' for transcript retrieval, 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?

No annotations are provided, and the description does not disclose behavioral traits such as authentication, rate limits, or side effects. It does describe the return format but lacks further context for a low-risk 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?

Extremely concise with two sentences, front-loaded with purpose, and every sentence adds 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?

Given no output schema and zero parameters, the description fully explains what the tool lists and how to proceed for details, making it complete for a simple 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?

No parameters exist; the description adds value by implicitly stating the output includes slug and title, which is sufficient for a parameterless tool.

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 lists Celestia whitepapers and research PDFs, specifying output includes slug and title, and explicitly distinguishes itself from tools for arbitrary web PDFs.

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 guidance: use for Celestia-specific PDFs, not for arbitrary web PDFs (pointing to web-search tools), and mentions the sibling tool get_whitepaper for reading one by 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 provided, so description carries full burden. It indicates the tool does exact-term/full-text search (not semantic), which is helpful. However, lacks details on pagination, result format, or potential side effects. Still, the behavior is clear enough for safe selection.

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?

Description is reasonably concise given the complexity. It front-loads the main purpose and then provides usage guidance. Two sentences dedicated to what not to use, which is valuable but could be slightly tighter.

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 complexity (many sources, many sibling tools), the description is highly complete. It explains scope, alternatives, and when to use other tools. No output schema, but search results are typically self-explanatory.

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 only 33% (only query described). Description adds context by listing query sources implicitly but does not detail the 'type' enum or 'limit' parameter. Partially compensates for low coverage but not fully.

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 performs keyword/full-text search over a specific knowledge base, listing sources. It distinguishes itself from siblings by specifying exact-term vs semantic search (semantic_search) and page retrieval (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 provides when-not-to-use guidance: not for other blockchains, general web, other knowledge bases, or local files. Also directs to alternatives like semantic_search for conceptual questions and get_doc for full page reading.

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 must compensate. It does not detail return format, rate limits, or idempotency. However, it clarifies scope and intended use. Lacks explicit behavioral traits.

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, well-structured paragraph. Concise yet comprehensive, with clear separation of purpose, exclusions, and alternatives. 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 and three parameters, the description covers purpose, boundaries, and parameter hints. Lacks output format description, but enough for an agent to infer basic usage.

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 67% but description adds value by explaining the query is free-form and listing the source types, which maps to the source_types parameter. However, it does not add detail on the limit parameter beyond schema defaults.

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 does 'Semantic (meaning-based) search over the Celestia knowledge base', listing specific sources. It distinguishes 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?

Explicitly says 'do NOT use for other blockchains', providing alternative tools (Ethereum docs, web-search, dedicated RAG). Also advises when to use this vs 'search' and 'get_doc'.

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