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

Despite no annotations, the description fully covers behavioral traits: write operation, environment requirement, return fields including readiness score categories, and optional intro post. No contradictions with annotations (none provided).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the main purpose, followed by return details and usage guidance. Every sentence adds value; no redundancy. Efficiently packed into a few sentences.

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?

Comprehensive for a 7-parameter tool with no output schema: covers return structure, readiness categories, and preconditions. Lacks error handling details, but acceptable given the tool's 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?

Schema coverage is 100%, so baseline is 3. The description adds value by explaining how parameters interact (e.g., capabilities override template defaults, intro_post helps build activity score). This extra context elevates it slightly beyond 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 explicitly states the tool creates an agent entity with W3C DID, applies a capability template, and returns a readiness report. It distinguishes from the sibling 'register_agent' by noting this is for full onboarding in one call.

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 clear guidance: 'Use this instead of register_agent when you want full onboarding in a single call.' Also specifies the required environment variable AGENTGRAPH_API_KEY, giving agents clear context for calling this 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 the description carries full burden. It discloses that all actions are idempotent, safe to call multiple times, and that it's a write operation. It also describes the effect of each action, providing transparency about behavior.

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 well-structured with a clear purpose up front, followed by action details and notes. It is somewhat lengthy but each sentence adds value. Could be slightly more concise, but still effective.

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 description covers all necessary information: purpose, actions with effects, parameter details (including examples), idempotence, auth requirement, and return value format. It fully compensates for the lack of output schema, making it complete for an AI agent.

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% (all parameters described), so baseline is 3. The description adds value by explaining each action's purpose, noting that intro_text is required for intro_post, and providing examples for agent_id and actions.

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 purpose: 'Execute trust-building actions for a bot on AgentGraph to improve its trust score.' It lists the three available actions and distinguishes from sibling tools like bot_bootstrap and register_agent by noting it should be used after them.

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 guidance: 'Use after bot_bootstrap or register_agent to build trust quickly.' It notes idempotence and the requirement for AGENTGRAPH_API_KEY. While it doesn't explicitly state when not to use, 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?

The disclosure 'read-only, requires AGENTGRAPH_API_KEY' transparently communicates the tool's safety and authentication needs. It also details the return structure, enhancing transparency beyond the absence of annotations.

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 concise with four sentences, each adding value. Key information is front-loaded: purpose, return structure, auth requirement, and usage timing.

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 one parameter and no output schema, the description covers purpose, return fields, auth, and timing comprehensively. Lacks explanation of 'AgentGraph' but overall complete for this simplicity level.

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%, so the baseline is 3. The description mentions 'agent_id' implicitly by advising use after registration but adds no additional meaning beyond the schema's UUID 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 tool description clearly states it checks a bot's readiness score on AgentGraph, specifying the resource and action. It distinguishes from siblings like 'bot_bootstrap' and 'bot_quick_trust' by focusing on readiness tracking.

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 advises using the tool 'after registration to track onboarding progress,' providing clear context. However, it does not explicitly mention when not to use it or list alternatives, leaving slight ambiguity.

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, description fully discloses behavior: 'Read-only network call to AgentGraph API, no authentication required, no side effects.' Also explains trust thresholds per interaction type.

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 concise (three sentences) and well-structured: purpose, output, thresholds, usage context. No redundant wording.

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 or annotations, the description covers all necessary aspects: purpose, output fields, behavioral traits, parameter details, and usage guidance. Complete for a tool of this 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% with detailed descriptions. The description adds value by providing specific threshold numbers for each interaction type (e.g., delegate threshold 0.6), which are absent from 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 checks safety of interacting with another agent based on trust scores, specifying output fields and differentiating interaction types. It references sibling tools like lookup_identity for obtaining IDs, distinguishing its role.

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 before delegating tasks, sending payments, or collaborating with agents you have not interacted with before.' Provides context on when to use, though does not explicitly state when not to use.

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 read-only, no auth required, potential 60s delay for first scan, and return value composition. No annotations exist, so description carries burden. Lacks details on error handling if both params provided 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?

Four sentences, each informative: purpose, return format, parameter guidance, usage context, timing info. 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?

Covers all aspects: purpose, input, output (return values), behavior (read-only, timing), and usage guidance. No output schema, but description sufficiently explains return. Completeness is high.

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% with descriptions. Description adds value by clarifying mutual exclusivity and contexts for each parameter (AgentGraph entities vs any repo).

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 verb 'Check' and resource 'security posture of an agent or GitHub repo'. It distinguishes from siblings by specifying vulnerability findings categories and trust score, which is unique among related 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 'Use before installing or interacting with third-party tools' and 'Provide either entity_id OR github_url'. Could mention not to use for other purposes or when to use alternatives, but current guidance is 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?

Describes outputs (trust score, tier, rate limits, JWS) and states no authentication. However, with no annotations, the description could better disclose caching behavior (force parameter hints at it) and whether the operation is idempotent.

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 redundant information, front-loaded with purpose and outputs. Highly 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 no output schema, the description enumerates return values well. It lacks details on error handling for missing repos, but overall provides sufficient context for an agent to invoke the 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 all three parameters with descriptions. The tool description adds no additional meaning beyond what the schema provides, resulting in a baseline score.

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 tool scans a GitHub repository and returns trust tier, rate limits, and attestation. It distinguishes from siblings like 'check_security' and 'verify_trust' by specifying the use case of checking any tool or agent before running.

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 'No authentication required' and 'Use this to check any tool or agent before running it.' This provides clear when-to-use context, but does not explicitly exclude scenarios where alternatives might be better.

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?

The description discloses key behavioral traits: 'Read-only network call to AgentGraph API, no authentication required, no side effects.' It also explains the badge auto-updates. However, it does not mention potential error conditions (e.g., invalid entity_id), but given no annotations, this is a minor gap.

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 concise (4 sentences), front-loaded with the main purpose, and each sentence adds value: output details, auto-update behavior, and usage context. No unnecessary 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 (single parameter, no output schema), the description covers all essential aspects: what it does, what it returns, behavior (auto-update, read-only, no auth), and when to use it. It is complete 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?

Schema coverage is 100% with a detailed parameter description for entity_id. The description adds no additional parameter information beyond the schema, so a baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Get an embeddable trust badge URL for an AgentGraph entity.' It specifies the action (get URL), resource (trust badge for an AgentGraph entity), and what is returned (JSON with badge_url, markdown, html). This differentiates it from sibling tools by positioning it as a post-processing step after verify_trust or lookup_identity.

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 guidance: 'Use after verify_trust or lookup_identity to generate a visual trust indicator for documentation or dashboards.' It also states that the tool is a read-only network call with no authentication required, helping the agent decide when to invoke it appropriately.

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, the description fully discloses behavior: 'Read-only network call to AgentGraph API, no authentication required, no side effects. Typical response time under 500ms.' It also mentions edge cases ('Returns null fields if entity not found'). This satisfies the transparency requirement completely.

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 well-structured with separate sentences for purpose, return fields, behavior, usage, and edge cases. It is concise without unnecessary words, earning a score of 4 (a 5 would require even tighter phrasing, but this is already 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 the single parameter and lack of output schema, the description is thorough: it covers the search query types, return fields, behavioral traits, network call details, typical response time, and null-handling behavior. Nothing essential is missing for the tool's 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?

The input schema already describes the query parameter with 100% coverage. The description adds meaningful detail: 'DID lookup is exact match; name lookup uses case-insensitive prefix search.' This provides nuance beyond the schema, though the schema is already descriptive.

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: 'Look up an entity on AgentGraph by DID or display name.' It specifies the verb, resource, and input method, and distinguishes itself from siblings by indicating it is a prerequisite for tools like verify_trust and check_interaction_safety.

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 to resolve an agent's identity before checking trust with verify_trust or check_interaction_safety.' This tells when to use the tool, though it does not explicitly state when not to use it or list alternatives. However, given the sibling tool names, 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?

With no annotations provided, the description carries the full burden. It discloses that the operation is a write, requires an API key environment variable, and outputs a specific JSON structure. It also mentions the agent's trust score trajectory post-registration. However, it lacks details on idempotency, error conditions, or rate limits, which would make it more 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 concise with three sentences, front-loading the primary purpose and then adding details. Every sentence adds value without redundancy or unnecessary text.

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 lacking an output schema, the description explains the return fields (agent_id, did_web, api_key, claim_token) and notes the initial trust score behavior. For a registration tool with 3 parameters and no output schema, this covers inputs and outputs adequately.

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 input schema already has 100% coverage with well-described parameters. The description adds no additional per-parameter meaning beyond the schema, but it does contextualize the overall return values (e.g., 'Returns JSON with agent_id...'), which indirectly aids understanding. Per guidelines, baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool registers a new AI agent with a W3C DID on AgentGraph, specifying the verb 'Register' and the resource 'AI agent with DID'. It also differentiates from sibling 'bot_bootstrap' by mentioning an alternative use case, 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 provides explicit guidance on when to use this tool versus the alternative 'bot_bootstrap', stating 'Use bot_bootstrap instead if you want one-call onboarding...'. It also notes prerequisites (AGENTGRAPH_API_KEY env var) and that it's a write operation, enabling the agent to decide correctly.

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 fully carries the burden. It clearly states 'Read-only, no auth required,' which are critical behavioral traits. Additionally, it details the output format (JSON with specific fields), compensating for the lack of an output schema. No contradictions.

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, front-loaded with the core purpose, then output details, then behavioral traits and usage. Every sentence provides essential information without redundancy. It is highly 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 read-only tool with two parameters and no output schema, the description is largely complete. It covers purpose, return structure, usage context, and parameter semantics. It does not mention error handling or edge cases, but for a straightforward query, this is acceptable.

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 input schema has 100% coverage with descriptions for both parameters. The description adds further value by providing an example UUID for entity_id and common thresholds for min_trust (e.g., 0.1, 0.3, 0.6 with meanings). This enhances understanding beyond the schema alone.

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 purpose: 'Verify an entity's trust score on AgentGraph'. It also lists the output fields (trust_score, trust_tier, grade, component breakdown) and suggests when to use it ('before interacting with unknown agents'). However, it does not explicitly differentiate from sibling tools like check_trust_tier or get_trust_badge, missing a chance to clarify its unique role.

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 includes explicit usage guidance: 'Use before interacting with unknown agents to assess risk.' This clearly indicates when to use the tool. However, it does not provide when-not-to-use scenarios or alternative tools, which would strengthen this dimension.

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