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

With no annotations, the description carries full burden but only states the action without behavioral details. It doesn't disclose permissions needed, side effects, error handling, or response format, leaving significant gaps for a mutation tool.

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

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

The description is a single, efficient sentence with no wasted words, front-loading the core action. It's appropriately sized for the tool's complexity, making it easy to parse quickly.

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 a mutation tool with 4 parameters, 0% schema coverage, no annotations, and an output schema (which reduces need for return value explanation), the description is incomplete. It lacks parameter semantics, behavioral context, and usage guidelines, making it inadequate for effective tool selection and invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate but adds no parameter details. It mentions 'typed graph edge' and 'two entities', hinting at 'relationship_type', 'from_entity', and 'to_entity', but fails to explain 'relationship_id' or any parameter meanings, leaving most semantics unclear.

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

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

The description clearly states the action ('Create') and resource ('a typed graph edge between two entities'), making the purpose understandable. It distinguishes from siblings like 'connect_entities' or 'list_relationships' by specifying creation of a typed edge, though it could be more explicit about differences.

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?

No guidance is provided on when to use this tool versus alternatives like 'connect_entities' or 'upsert_entity', nor are prerequisites or exclusions mentioned. The description implies usage for creating relationships but lacks context for decision-making.

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 of behavioral disclosure. The phrase 'Add a piece of content' implies a write/mutation operation, but the description fails to disclose critical behavioral traits such as whether this requires specific permissions, if it's idempotent, what happens on conflicts, or how it interacts with existing content. For a mutation tool with zero annotation coverage, this is a significant 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 extremely concise at just 7 words in a single sentence. While this brevity comes at the cost of completeness, every word directly contributes to stating the core action without any fluff or redundant phrasing. It's front-loaded with the essential verb and object.

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 a mutation tool with 4 parameters, 0% schema description coverage, no annotations, and multiple similar sibling tools, the description is completely inadequate. While an output schema exists (which reduces the need to describe return values), the description fails to provide necessary context about the tool's purpose, usage, behavior, or parameters. This leaves the agent poorly equipped to use the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, meaning none of the 4 parameters have descriptions in the schema. The tool description provides no information about parameter meanings, expected formats, or usage examples. It doesn't explain what 'entity_id', 'content_type', 'body', or 'content_id' represent, leaving all parameters completely undocumented.

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 'Add a piece of content to an entity' is a tautology that essentially restates the tool name 'append_content' with minimal elaboration. It specifies the verb 'add' and resource 'content to an entity', but lacks specificity about what 'content' means or how it differs from sibling tools like 'write_content' or 'update_entity'. This provides only basic direction without clear 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?

No guidance is provided on when to use this tool versus alternatives. With sibling tools like 'write_content', 'update_entity', and 'create_entity' available, the description offers no context about appropriate use cases, prerequisites, or exclusions. This leaves the agent without necessary decision-making information.

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 of behavioral disclosure. It states 'Tune SQLite settings,' implying a write/mutation operation that changes database configuration, but doesn't disclose critical traits: whether this requires specific permissions, if changes are persistent or reversible, potential side effects on database performance or stability, rate limits, or what the output contains. For a tool with 6 parameters and no annotation coverage, this is a significant gap in transparency.

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

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

The description is a single, efficient sentence that front-loads the core action ('Tune SQLite settings') and purpose ('for throughput during heavy project-memory workloads'). There is zero wasted text, and every word contributes to understanding the tool's intent without redundancy or unnecessary elaboration.

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 complexity (6 parameters, mutation operation) and the presence of an output schema (which relieves the description from explaining return values), the description is minimally adequate but incomplete. It covers the high-level purpose but lacks usage guidelines, parameter details, and behavioral context needed for safe and effective use. With no annotations and 0% schema coverage, the description should do more to guide the agent, especially for a configuration-changing 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 description coverage is 0%, meaning none of the 6 parameters have descriptions in the schema. The description adds no parameter-specific information beyond the general context of 'SQLite settings for throughput.' It doesn't explain what journal_mode, synchronous, temp_store, cache_size, mmap_size, or automatic_index do, their valid values, or how they relate to performance tuning. The description fails to compensate for the lack of schema documentation.

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 ('Tune') and resource ('SQLite settings') with a specific purpose ('for throughput during heavy project-memory workloads'). It distinguishes from siblings by focusing on performance tuning rather than data operations like create_entity or run_read_query. However, it doesn't explicitly differentiate from potential similar tools like get_database_health, which might provide diagnostics rather than configuration changes.

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 implies usage during 'heavy project-memory workloads' but provides no explicit guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., database connection state), exclusions (e.g., not for read-only scenarios), or compare to siblings like get_database_health for diagnostics. The context is vague, leaving the agent to infer appropriate usage scenarios.

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 of behavioral disclosure. It mentions that archiving preserves 'history or related project memory,' which adds some context about data retention. However, it does not cover critical aspects such as required permissions, whether the action is reversible, rate limits, or what the archived status entails, leaving significant gaps for a mutation tool.

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

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

The description is a single, well-structured sentence that efficiently conveys the core action and key distinction from deletion. It is front-loaded with the main purpose and avoids unnecessary details, making it highly concise and effective for quick understanding.

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 that there is an output schema (which handles return values), no annotations, and low schema coverage, the description is moderately complete. It clarifies the tool's purpose and data retention aspect, but as a mutation tool with three parameters, it lacks details on behavioral traits and parameter meanings, making it somewhat incomplete for safe and effective use by 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 description coverage is 0%, so the schema provides no parameter details. The description does not mention any parameters explicitly, such as 'entity_id,' 'reason,' or 'archived_status,' nor does it explain their meanings or usage. With three parameters (one required) and no compensation in the description, the baseline score of 3 reflects minimal adequacy, as the description fails to add semantic value beyond the schema's structure.

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

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

The description clearly states the action ('Archive') and the resource ('an entity'), and distinguishes it from deletion by specifying 'without deleting its history or related project memory.' This differentiates it from sibling tools like 'delete_entity' and 'update_entity,' making the purpose specific and 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 implies usage by contrasting with deletion ('without deleting its history or related project memory'), suggesting it's for preserving data while marking it as archived. However, it lacks explicit guidance on when to use this tool versus alternatives like 'delete_entity' or 'update_entity,' and does not mention prerequisites or exclusions, leaving some context to inference.

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 of behavioral disclosure. While 'Initialize' implies a creation/setup operation, the description doesn't reveal whether this is idempotent, what happens if entities already exist, what permissions are required, or what the 'standard memory-area anchor entities' actually are. For a tool with no annotation coverage, this leaves significant behavioral questions unanswered.

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, efficient sentence that gets straight to the point with no wasted words. It's appropriately sized for what it communicates, though what it communicates is limited.

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 an output schema (which reduces the need to describe return values), but with 4 parameters at 0% schema coverage and no annotations, the description should do more to explain parameter meanings and behavioral context. For an initialization tool that likely creates multiple entities, the description is minimally adequate but leaves important questions unanswered about what exactly gets created and how.

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%, meaning none of the 4 parameters have descriptions in the schema. The tool description provides no information about what 'project_id', 'project_name', 'description', or 'tags' mean in this context, nor does it explain how they relate to the initialization process. The description fails to compensate for the complete lack of schema documentation.

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

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

The description clearly states the action ('Initialize') and the resources ('project root entity and standard memory-area anchor entities'), making the purpose understandable. However, it doesn't explicitly differentiate this from sibling tools like 'create_entity' or 'set_project_root', which appear related to entity/project creation and configuration.

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 no guidance on when to use this tool versus alternatives. There are several sibling tools that seem related (e.g., 'create_entity', 'set_project_root', 'get_project_context'), but the description doesn't indicate when this initialization is preferred or required over those other operations.

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

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

With no annotations, the description carries full burden. It discloses the idempotent 'create-if-missing' behavior and return of existing edges, which is valuable. However, it lacks critical details: permissions needed, whether creation is reversible, rate limits, or what 'return the existing edge' entails (e.g., format, errors). For a mutation tool with zero annotation coverage, this is insufficient.

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, efficient sentence with zero waste. It's front-loaded with the core action and conditional behavior, making it easy to parse quickly.

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 a mutation tool with 4 parameters (0% schema coverage), no annotations, but an output schema exists, the description is moderately complete. It covers the idempotent behavior but misses parameter explanations and broader context like error handling or side effects. The output schema mitigates some gaps in return values.

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%, so the description must compensate. It mentions 'relationship' but doesn't explain any of the 4 parameters (e.g., what 'from_entity' and 'to_entity' represent, valid 'relationship_type' values, or purpose of 'relationship_id'). This leaves parameters largely undocumented.

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

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

The description clearly states the action ('Create a relationship if missing') and resource ('relationship'), with a specific behavior ('otherwise return the existing edge'). It distinguishes from siblings like 'add_relationship' by implying idempotent behavior, though not explicitly named.

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?

No explicit guidance on when to use this tool versus alternatives like 'add_relationship' or 'list_relationships'. The description implies it's for ensuring a relationship exists, but doesn't specify prerequisites, constraints, or comparative use cases with siblings.

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 full burden for behavioral disclosure. It states 'Create' implying a write operation but doesn't cover permissions, side effects, error conditions, or what 'authoritative' means. This is inadequate for a mutation tool with 7 parameters and no annotation coverage.

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, efficient sentence with no wasted words. It's appropriately sized and front-loaded, making it easy to parse quickly without unnecessary elaboration.

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 (7 parameters, mutation operation, no annotations) and the presence of an output schema, the description is incomplete. It doesn't explain the creation process, what 'authoritative' entails, or how it differs from similar tools, leaving significant gaps for agent understanding.

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%, so the description must compensate by explaining parameters. It adds no meaning beyond the schema, failing to clarify what 'entity_id', 'entity_type', or other fields represent in context. This leaves most parameters semantically unclear.

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

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

The description clearly states the action ('Create') and resource ('authoritative project entity'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'get_or_create_entity' or 'upsert_entity', which appear to have overlapping functionality, so it doesn't reach the highest clarity level.

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 no guidance on when to use this tool versus alternatives like 'get_or_create_entity', 'upsert_entity', or 'update_entity'. It doesn't mention prerequisites, constraints, or typical use cases, leaving the agent with minimal 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 are provided, so the description carries full burden for behavioral disclosure. It states the tool creates a snapshot but doesn't explain what 'project state' entails, whether this is a destructive/irreversible operation, permission requirements, rate limits, or what happens on success/failure. This is inadequate for a mutation tool.

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

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

The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part earns its place, making it easy to parse quickly.

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 annotations, 0% schema coverage, and 4 parameters (2 required), the description is incomplete. It lacks behavioral details (e.g., side effects), parameter explanations, and context for a mutation tool, though the presence of an output schema slightly mitigates the need to describe return values.

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%, so the description must compensate by explaining parameters. It mentions 'named snapshot' which hints at the 'name' parameter but doesn't clarify 'snapshot_id', 'description', or 'entity_ids' (e.g., whether they filter entities or define scope). The description adds minimal value 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 action ('Create') and resource ('named snapshot of current project state'), making the purpose immediately understandable. However, it doesn't differentiate this tool from potential siblings like 'get_snapshot' or 'archive_entity' that might also involve snapshots, preventing a perfect score.

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?

No guidance is provided on when to use this tool versus alternatives. With siblings like 'get_snapshot' (likely for retrieval) and 'archive_entity' (possibly for storage), the description lacks context on appropriate use cases, prerequisites, or exclusions.

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 of behavioral disclosure. It adds valuable context about guardrails, prerequisites (archiving, no critical dependents), and the force parameter's effect. However, it doesn't cover aspects like permissions needed, irreversibility, error handling, or what the output schema returns, leaving gaps for a destructive 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?

The description is a single, well-structured sentence that front-loads the core action ('Delete an entity with guardrails') and efficiently adds critical qualifications. Every word earns its place, with no redundancy or fluff, making it highly concise and easy to parse.

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 (destructive operation with guardrails), no annotations, and an output schema (which reduces need to describe returns), the description is moderately complete. It covers key behavioral aspects like prerequisites and the force parameter, but lacks details on permissions, error cases, or what constitutes 'critical dependents', which could hinder safe invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It effectively explains the semantics of the 'force' parameter by contrasting non-forced deletion (requires archiving and no critical dependents) with implied forced deletion. It doesn't detail 'entity_id' beyond what the schema title implies, but the added context for 'force' significantly enhances understanding beyond the bare 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 ('Delete') and resource ('an entity'), making the purpose unambiguous. It distinguishes this from other tools by specifying deletion with guardrails, though it doesn't explicitly differentiate from siblings like 'archive_entity' or 'merge_entities' beyond mentioning archiving as a prerequisite.

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 clear context for when to use this tool: for deletion with guardrails, requiring prior archiving and no critical dependents for non-forced deletion. It implies alternatives like archiving first, but doesn't explicitly name sibling tools or specify when-not-to-use scenarios beyond the prerequisites.

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 full burden. It discloses that deletion occurs and removal is recorded in project history, which adds some behavioral context. However, it lacks critical details like permissions needed, whether deletion is reversible, side effects on related entities, or error handling, leaving significant gaps for a destructive 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?

The description is a single, efficient sentence that front-loads the core action ('Delete a relationship by id') and adds secondary context ('record the removal in project history') without unnecessary words, making it highly concise and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's destructive nature and lack of annotations, the description is incomplete—it doesn't cover permissions, reversibility, or side effects. However, the presence of an output schema mitigates the need to explain return values, and it does mention project history recording, providing some context for a basic delete 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 description adds minimal semantics beyond the input schema, which has 0% description coverage. It implies 'relationship_id' identifies the relationship to delete, but doesn't clarify format, constraints, or examples. With low schema coverage, this doesn't fully compensate, but provides basic context for the single parameter.

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

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

The description clearly states the action ('Delete a relationship by id') and specifies the resource ('relationship'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'delete_entity' or 'list_relationships', which would require a 5.

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 no guidance on when to use this tool versus alternatives like 'delete_entity' or 'list_relationships'. It mentions recording removal in project history, which hints at context, but doesn't specify prerequisites, exclusions, or explicit 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 full burden. It mentions the tool writes files (implied mutation) and requires user requests, but lacks details on permissions, file system impact, error handling, or output behavior. The description adds minimal behavioral context 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 a single, well-structured sentence that efficiently conveys the core action and usage condition. It's front-loaded with the main purpose and has zero wasted words, making it easy to parse.

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 6 parameters with 0% schema coverage, no annotations, and sibling tools like 'render_markdown_views', the description is incomplete. It doesn't explain what 'markdown views' are, how parameters interact, or what the output schema returns. The presence of an output schema reduces the need to describe returns, but other gaps remain significant.

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%, so the description must compensate for 6 undocumented parameters. It doesn't explain any parameters like 'view_names', 'output_dir', or 'user_requested', leaving their purposes unclear. The description adds no parameter semantics beyond the basic action.

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

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

The description states the tool 'Write[s] generated markdown views' which provides a verb ('write') and resource ('markdown views'), but it's vague about what 'markdown views' are and doesn't distinguish from siblings like 'render_markdown_views' or 'write_content'. The phrase 'human-readable artifact' adds some context but remains general.

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 'only after an explicit user request for a human-readable artifact,' providing clear when-to-use guidance. This distinguishes it from tools that might generate views automatically or for other purposes, though it doesn't name specific 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 mentions 'likely duplicate or related entities' but doesn't disclose behavioral traits such as how similarity is determined, whether it's a read-only operation, performance characteristics, or error handling. For a tool with no annotations, this leaves significant gaps in understanding its 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 a single, efficient sentence that front-loads the core purpose. There's no wasted language, and it directly communicates the tool's intent without unnecessary elaboration.

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 moderate complexity (3 parameters, 1 required) and the presence of an output schema, the description is minimally adequate. It states the purpose and usage context but lacks details on parameters and behavioral traits. The output schema mitigates some completeness issues, but the description should do more to compensate for the 0% schema coverage.

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%, so the description must compensate. It doesn't add any meaning beyond what the input schema provides—no explanation of what 'name', 'entity_type', or 'limit' represent semantically. The description fails to clarify parameter roles, leaving them undocumented.

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: 'Find likely duplicate or related entities before creating a new one.' It specifies the verb ('Find'), resource ('entities'), and context ('before creating a new one'). However, it doesn't explicitly differentiate from siblings like 'search_content' or 'resolve_entity_by_name', which might have overlapping functionality.

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 clear context for usage: 'before creating a new one.' This implies it should be used as a pre-creation check to avoid duplicates. It doesn't explicitly state when not to use it or name alternatives, but the context is sufficiently clear for an agent to infer appropriate usage.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states 'Return a compact architecture-oriented node and relationship summary,' which implies a read-only operation but lacks details on permissions, rate limits, output format, or any side effects. For a tool with zero annotation coverage, this is insufficient to inform safe and effective 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?

The description is a single, efficient sentence with no wasted words. It front-loads the core action ('Return') and key attributes ('compact architecture-oriented'), making it easy to parse quickly. Every element serves a purpose, earning a top score for conciseness.

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 has an output schema (which handles return values), no annotations, and low schema description coverage, the description is minimally adequate. It states what the tool does but lacks usage guidelines, detailed parameter semantics, and behavioral context. For a read operation with structured output, it meets basic needs but leaves significant gaps in agent guidance.

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%, so the schema provides no parameter descriptions. The tool description mentions 'compact' and 'architecture-oriented,' which loosely relates to the 'compact' parameter and overall function but doesn't explain node_limit, relationship_limit, or how compact affects the output. It adds minimal semantic value beyond the schema's titles and types, 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?

The description clearly states the verb ('Return') and resource ('architecture-oriented node and relationship summary'), making the purpose understandable. It specifies 'compact' and 'architecture-oriented' which helps differentiate it from siblings like get_entity_graph or get_dependency_view. However, it doesn't explicitly contrast with all similar siblings, keeping it from a perfect score.

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 no guidance on when to use this tool versus alternatives like get_entity_graph, get_dependency_view, or list_entities. It mentions 'compact' and 'architecture-oriented' but doesn't specify scenarios or exclusions, leaving the agent with minimal context for tool 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?

With no annotations provided, the description carries full burden for behavioral disclosure. It states what the tool reports but doesn't describe how it works: whether it's a read-only diagnostic, if it requires specific permissions, what format the output takes, or if it has performance implications. For a health-check tool with zero annotation coverage, this leaves significant behavioral questions unanswered.

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 extremely concise - a single sentence that efficiently lists the three report categories. Every word earns its place, and the structure is front-loaded with the core purpose. There's zero waste or redundancy in this compact description.

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 that there's an output schema (which should document return values), the description's main job is to explain purpose and usage. It adequately states what the tool reports but lacks context about when to use it versus siblings, and with no annotations, it doesn't address behavioral aspects. For a diagnostic tool with output schema, this is minimally adequate but has clear 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?

The description mentions no parameters at all, while the schema has one parameter ('limit') with 0% description coverage. Since schema_description_coverage is 0%, the description should compensate but doesn't. The baseline is 3 because the tool has only one parameter, but the description adds no semantic information about what 'limit' controls or how it affects the health report.

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: to report three specific types of issues (duplicates, low-quality records, retention pressure) in project memory. It uses specific verbs ('report') and identifies the resource ('project memory'), but doesn't explicitly differentiate from siblings like 'get_architecture_summary' or 'get_recent_activity' that might also provide health-related insights.

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 no guidance on when to use this tool versus alternatives. With many sibling tools like 'get_architecture_summary', 'get_recent_activity', and 'prune_content_retention' that might address similar concerns, there's no indication of when this specific health report is appropriate versus other diagnostic or maintenance tools.

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 full burden for behavioral disclosure. It mentions what's returned ('decisions and recent supporting note excerpts') and a constraint ('without requiring ad hoc SQL'), but doesn't describe important behavioral aspects: whether this is read-only (implied but not stated), what format the data returns, whether there are rate limits, authentication requirements, or how 'recent' is defined. For a tool with no annotation coverage, this leaves significant gaps.

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, efficient sentence that communicates the core purpose and key differentiator. Every word earns its place: 'Return' (action), 'decisions and recent supporting note excerpts' (what's returned), 'without requiring ad hoc SQL' (key benefit/differentiator). There's no wasted verbiage 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?

Given the tool has an output schema (which handles return value documentation) but zero annotation coverage and 0% schema description coverage for 3 parameters, the description is incomplete. It adequately states the purpose but fails to address parameter semantics or important behavioral context. For a data retrieval tool with multiple parameters, the description should do more to compensate for the lack of structured documentation.

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?

With 0% schema description coverage and 3 parameters, the description provides no information about parameters. It doesn't mention 'limit', 'offset', or 'compact' at all, nor does it explain what these parameters control. The description doesn't compensate for the complete lack of schema documentation, leaving all parameters semantically undefined in the description text.

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: 'Return decisions and recent supporting note excerpts' - a specific verb ('Return') and resources ('decisions', 'recent supporting note excerpts'). It distinguishes from siblings by noting it 'without requiring ad hoc SQL', which differentiates it from query tools like 'run_read_query' or 'query_view'. However, it doesn't explicitly contrast with other decision/log-related tools (none appear in the sibling list).

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 implies usage context by stating it provides decisions with note excerpts 'without requiring ad hoc SQL', suggesting this is a simplified alternative to raw query tools. However, it doesn't explicitly state when to use this tool versus alternatives like 'get_recent_activity' or 'get_recent_reasoning', nor does it provide any exclusion criteria or prerequisites. The guidance is implied rather than explicit.

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 mentions 'stable compact fields' which hints at output behavior, but doesn't disclose critical traits like whether this is a read-only operation, potential performance impacts, rate limits, or authentication needs. For a graph data tool with 5 parameters, this is insufficient behavioral context.

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

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

The description is a single, efficient sentence that gets straight to the point. There's no wasted verbiage or redundancy. However, it could be more front-loaded with clearer purpose before mentioning implementation details like 'stable compact fields'.

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 has an output schema (which handles return values) but no annotations and 0% schema description coverage, the description is minimally adequate. It identifies the tool as returning graph data but lacks sufficient context about when and how to use it effectively, especially compared to sibling tools. For a 5-parameter tool with graph operations, more guidance would be helpful.

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%, so the description must compensate. It mentions 'dependency-oriented' which relates to 'relationship_types', and 'compact' which maps to the 'compact' parameter, but doesn't explain the meaning of 'root_entity_id', 'max_depth', or 'limit'. With 5 parameters and no schema descriptions, this leaves significant gaps in understanding parameter purposes.

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

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

The description states the tool returns 'dependency-oriented graph data' which indicates its purpose, but it's vague about what specific resource or entity this applies to. It doesn't clearly distinguish from sibling tools like 'get_entity_graph' or 'query_view' which might also return graph data. The phrase 'stable compact fields' adds some specificity but remains abstract.

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?

No guidance is provided on when to use this tool versus alternatives like 'get_entity_graph' or 'query_view'. The description doesn't mention prerequisites, context, or exclusions. It's left to the agent to infer usage based on the tool name and parameters alone.

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 mentions fetching and optional inclusion of metadata, content, and events, but doesn't disclose critical behaviors such as authentication requirements, rate limits, error handling, or whether it's a read-only operation. This leaves significant gaps for a tool that likely interacts with a database or API.

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, efficient sentence that front-loads the main action ('Fetch an entity') and adds optional details. There's no wasted wording, though it could be slightly more structured by explicitly listing the related data types.

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 complexity of fetching an entity with optional related data, no annotations, and an output schema (which reduces the need to describe return values), the description is minimally adequate. However, it lacks details on permissions, data formats, and error cases, which are important for a tool in a server with many data manipulation siblings.

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%, so the schema provides no parameter descriptions. The description adds some value by explaining that 'include_related' controls metadata, content, and events, but it doesn't clarify the format or scope of 'entity_id' or the default behavior when 'include_related' is true. This partial compensation results 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?

The description states the tool fetches an entity and optionally includes related data, which clarifies the basic action. However, it doesn't specify what type of entity (e.g., project, task, document) or distinguish it from similar sibling tools like 'get_entity_graph' or 'resolve_entity_by_name', making it somewhat vague.

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?

No guidance is provided on when to use this tool versus alternatives. With many sibling tools like 'list_entities', 'get_entity_graph', and 'resolve_entity_by_name', the description lacks any context for selection, leaving the agent to infer usage based on the name alone.

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 of behavioral disclosure. It mentions 'traverse outward relationship dependencies,' which implies a read-only operation, but doesn't clarify if it's safe, what permissions are needed, or how it handles limits like 'max_depth' or 'edge_limit.' For a tool with 6 parameters and no annotation coverage, this is a significant gap in describing its behavior and constraints.

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, efficient sentence: 'Traverse outward relationship dependencies from an entity.' It's front-loaded with the core purpose and wastes no words, making it highly concise and well-structured for quick understanding.

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 (6 parameters, no annotations, but with an output schema), the description is minimally adequate. The output schema likely covers return values, reducing the need for that in the description. However, for a tool that traverses relationships with multiple configurable limits, the description lacks details on behavior, error handling, or output format, leaving gaps in context.

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%, meaning none of the 6 parameters are documented in the schema. The description adds no meaning beyond the schema—it doesn't explain what 'entity_id' refers to, how 'max_depth' affects traversal, what 'relationship_type' entails, or the purpose of 'compact.' With low coverage and no compensation in the description, the parameters remain largely unexplained.

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: 'Traverse outward relationship dependencies from an entity.' It specifies the verb ('traverse') and resource ('relationship dependencies from an entity'), making it understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_dependency_view' or 'list_relationships', which might offer similar functionality, so it doesn't reach the highest score.

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 no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_dependency_view' or 'list_relationships', nor does it specify prerequisites, contexts, or exclusions. This lack of usage guidance makes it harder for an AI agent to select the correct tool among similar options.

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 full burden. It discloses the core behavior (reuse or create with stable id), but misses critical details: what 'exact' matching entails (e.g., by name, type, or other fields), whether creation is idempotent, permission requirements, error handling, or rate limits. For a mutation tool with 7 parameters, this is insufficient.

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, efficient sentence that front-loads the core purpose. Every word earns its place: 'reuse' and 'create' define the dual behavior, 'exact entity' hints at matching logic, and 'generated stable id' clarifies the creation aspect. No wasted words 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?

Given complexity (7 parameters, mutation operation, no annotations) and an output schema (which helps), the description is incomplete. It doesn't explain parameter roles, matching criteria, or behavioral nuances like idempotency. For a tool that both reads and writes, more context is needed to use it correctly without trial and error.

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%, so the description must compensate. It mentions 'entity' and 'stable id' but doesn't explain any of the 7 parameters (e.g., what 'entity_type' or 'attributes' mean, how 'name' is used for matching). The description adds minimal value beyond the schema's property names, leaving parameters largely undocumented.

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: 'Reuse an exact entity when it already exists or create one with a generated stable id.' It specifies the verb ('reuse' or 'create') and resource ('entity'), and distinguishes it from siblings like 'create_entity' (which only creates) and 'get_entity' (which only retrieves). However, it doesn't explicitly mention what constitutes an 'exact' match, leaving some ambiguity.

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 implies usage context: use this when you want to ensure an entity exists, reusing it if possible. It distinguishes from 'create_entity' by hinting at idempotent behavior. However, it lacks explicit guidance on when to choose this over alternatives like 'upsert_entity' or 'resolve_entity_by_name', and doesn't mention prerequisites or exclusions.

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 of behavioral disclosure. It implies a read-only operation by using 'Return', which is consistent with typical getter functions, but does not specify permissions, side effects, or response format. It adds minimal context beyond the basic action, missing details like error handling or data freshness.

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, efficient sentence that front-loads the key action and resources. It avoids redundancy and wastes no words, making it easy to parse and understand quickly.

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 (0 parameters, no annotations, but has an output schema), the description is reasonably complete. It specifies what is returned, and since an output schema exists, it need not detail return values. However, it could improve by mentioning typical use cases or integration with sibling tools for better context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters, and the input schema has 100% description coverage with an empty object. The description appropriately does not discuss parameters, as none exist, and focuses on the output. This aligns with the baseline for zero parameters, providing clarity without unnecessary detail.

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

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

The description clearly states the action ('Return') and the specific resources returned ('current project root, DB path, and export directory'), making the purpose explicit. However, it does not differentiate from sibling tools like 'server_info' or 'get_database_health', which might provide overlapping or related information, so it misses full distinction.

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 no guidance on when to use this tool versus alternatives, such as 'server_info' for general server details or 'get_database_health' for database status. It lacks context on prerequisites, exclusions, or specific scenarios for invocation, leaving usage unclear.

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 for behavioral disclosure. The description mentions returning 'recent entities, content, and events' but doesn't specify what 'recent' means (time window, recency criteria), whether results are paginated, sorted, or filtered. It also doesn't mention authentication requirements, rate limits, or potential side effects. For a tool with no annotation coverage, this leaves significant behavioral gaps.

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, efficient sentence that gets straight to the point. It's appropriately sized for a simple retrieval tool, with no wasted words or unnecessary elaboration. The structure is front-loaded with the core functionality. It could be slightly more specific but doesn't suffer from verbosity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that there's an output schema (which should document return values), the description doesn't need to explain return format. However, with no annotations and 0% schema description coverage, the description should do more to explain what 'recent activity' encompasses and how it differs from other retrieval tools. For a tool with three parameters and many similar siblings, the description is minimally adequate but leaves important contextual gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the schema provides no parameter descriptions. The tool description doesn't mention any parameters at all, leaving all three parameters (limit, offset, compact) completely undocumented. However, with only three parameters and default values provided in the schema, the baseline is 3 since the agent can infer basic usage from parameter names and defaults, though the description adds no value.

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

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

The description states the tool 'returns recent entities, content, and events' which provides a general purpose, but it's vague about what specific resources or scope it covers. It mentions helping 'an AI resume context quickly' which adds some context but doesn't clearly differentiate this tool from other list/query siblings like list_entities, get_entity, or search_content. The purpose is understandable but lacks specificity.

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 no guidance on when to use this tool versus alternatives. With many sibling tools for retrieving data (list_entities, get_entity, search_content, get_entity_graph, etc.), there's no indication of when this 'recent activity' tool is preferred over those other options. The mention of 'resume context quickly' implies a specific use case but doesn't articulate when this is the right choice.

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 of behavioral disclosure. It mentions 'quick AI context recovery,' which implies a read-only operation for efficiency, but fails to detail critical aspects such as permissions, rate limits, data freshness, or what 'reasoning excerpts' entail. This leaves significant gaps for a tool with no annotation coverage.

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, efficient sentence that is front-loaded with the core purpose. Every word contributes to understanding the tool's function without any redundancy or unnecessary elaboration.

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 moderate complexity (3 parameters, no annotations) and the presence of an output schema (which reduces the need to describe return values), the description is minimally adequate. However, it lacks details on parameters, behavioral traits, and usage context, making it incomplete for optimal agent understanding.

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 3 parameters with 0% description coverage, and the tool description provides no information about them. It doesn't explain what 'limit', 'offset', or 'compact' mean in the context of returning reasoning excerpts, failing to compensate for the schema's lack of documentation.

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 with a specific verb ('Return') and resource ('recent reasoning excerpts'), and provides a functional context ('for quick AI context recovery'). However, it doesn't explicitly differentiate from sibling tools like 'get_recent_activity' or 'get_decision_log' that might also retrieve recent information, which prevents a perfect score.

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 no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_recent_activity' or 'get_decision_log' that might serve similar purposes, nor does it specify prerequisites, exclusions, or optimal scenarios for usage.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'fetch' but doesn't specify whether this is a read-only operation, what permissions might be required, how it handles errors, or if there are rate limits. The description is too vague to fully inform the agent about 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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded with the core action and resource, making it easy to parse quickly, which is ideal for conciseness in tool descriptions.

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 has an output schema (which likely defines return values), the description doesn't need to explain outputs. However, with no annotations, 1 parameter at 0% coverage, and moderate complexity (fetching entities), the description is minimal—it covers the basic purpose but lacks depth on usage, behavior, or parameter details, making it adequate but incomplete for full agent understanding.

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 1 parameter with 0% description coverage, so the schema provides no semantic details. The description adds minimal context by implying 'snapshot_id' identifies a 'named snapshot', but it doesn't explain what a snapshot is, how to obtain its ID, or the format expected. This partial compensation aligns with the baseline 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 action ('fetch') and the target resource ('a named snapshot and its captured entities'), which is specific and informative. However, it doesn't explicitly differentiate this tool from similar siblings like 'get_entity' or 'get_entity_graph', which might also retrieve entity-related data, leaving room for ambiguity in sibling context.

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 no guidance on when to use this tool versus alternatives, such as 'get_entity' for individual entities or 'list_entities' for broader listings. It lacks context on prerequisites, exclusions, or typical scenarios, offering minimal usage direction beyond the basic purpose.

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 for behavioral disclosure. While 'List' implies a read operation, the description doesn't mention pagination behavior (though limit/offset parameters exist), sorting, performance characteristics, authentication requirements, or what happens when filters return no results. For an 8-parameter tool with no annotation coverage, this leaves significant behavioral gaps.

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, efficient sentence that front-loads the core purpose ('List entities') followed by the key filtering capabilities. Every word serves a purpose with zero redundancy or unnecessary elaboration. It's appropriately sized for a listing tool with multiple filter options.

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 has an output schema (which handles return values), 8 parameters with 0% schema coverage, and no annotations, the description provides basic purpose and filter enumeration but lacks behavioral context and parameter guidance. It's minimally adequate for understanding what the tool does but insufficient for optimal agent usage without additional schema documentation.

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%, so the description must compensate. It enumerates the filter types ('type, status, attribute, tag, and text') which maps to 5 of the 8 parameters, but doesn't explain 'limit' or 'offset' (pagination) or provide examples of valid values. The description adds some value by naming the filter categories but doesn't fully compensate for the complete lack of schema descriptions.

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 resource ('entities') with specific filtering capabilities ('optional type, status, attribute, tag, and text filters'). It distinguishes this as a listing/filtering tool rather than a creation or update operation. However, it doesn't explicitly differentiate from similar siblings like 'search_content' or 'find_similar_entities' beyond the 'list' terminology.

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 no guidance on when to use this tool versus alternatives. With siblings like 'search_content', 'find_similar_entities', and 'get_entity' available, there's no indication of when filtering via list_entities is preferred over other search/retrieval methods. No prerequisites, exclusions, or comparative context is mentioned.

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 of behavioral disclosure. It mentions listing graph edges with optional constraints, but fails to describe key behaviors: whether this is a read-only operation, if it requires permissions, how results are paginated or limited, or what the output format entails. For a tool with 4 parameters and no annotation coverage, this is a significant gap in transparency.

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

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

The description is a single, efficient sentence that front-loads the core action ('List graph edges') and adds optional details concisely. There's no wasted wording, and it's structured to convey essential information without fluff, making it easy to parse quickly.

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 (4 parameters, no annotations, but with an output schema), the description is minimally adequate. The output schema likely covers return values, reducing the need for output details in the description. However, it lacks behavioral context and full parameter semantics, making it incomplete for safe and effective use without additional inference or documentation.

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%, so the description must compensate for undocumented parameters. It only vaguely references 'entity and direction' constraints, covering at most 2 of the 4 parameters (entity_id and direction), but doesn't explain 'relationship_type' or 'limit'. This partial coverage leaves key parameters like 'limit' unexplained, failing to add sufficient meaning beyond the bare 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 ('List') and resource ('graph edges'), making the purpose understandable. It specifies the optional constraint ('to an entity and direction'), which adds clarity. However, it doesn't explicitly differentiate from sibling tools like 'get_entity_graph' or 'connect_entities', which might also involve graph relationships, so it doesn't reach the highest score.

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 no guidance on when to use this tool versus alternatives. It mentions optional constraints but doesn't specify scenarios, prerequisites, or exclusions. With many sibling tools like 'get_entity_graph' or 'list_entities', there's no indication of how this tool fits into the broader context, leaving usage unclear.

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 of behavioral disclosure. It states the tool lists view names but doesn't describe any behavioral traits such as whether it's read-only (implied by 'list'), what format the output takes, potential rate limits, authentication requirements, or error conditions. For a tool with zero annotation coverage, this is a significant gap in transparency.

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

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

The description is a single, efficient sentence: 'List available view names for query_view discovery.' It is front-loaded with the core purpose and includes no unnecessary words. Every part of the sentence earns its place by clarifying the tool's function and context.

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 (0 parameters) and the presence of an output schema (which handles return values), the description is minimally adequate. However, without annotations, it lacks details on behavioral aspects like safety, performance, or error handling. For a simple list tool, this is acceptable but leaves room for improvement in transparency.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters, and schema description coverage is 100%, so there are no parameters to document. The description doesn't need to add parameter semantics beyond what the schema provides. A baseline score of 4 is appropriate for zero-parameter tools, as there's no risk of missing parameter documentation.

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: 'List available view names for query_view discovery.' It specifies the verb ('List') and resource ('available view names'), and indicates the intended use case ('for query_view discovery'). However, it doesn't explicitly distinguish this tool from potential sibling alternatives like 'query_view' or 'export_markdown_views', which might also involve views.

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 implies usage context by mentioning 'for query_view discovery', suggesting this tool should be used to discover available views before using 'query_view'. However, it doesn't provide explicit guidance on when to use this tool versus alternatives (e.g., 'export_markdown_views' or 'render_markdown_views'), nor does it specify any prerequisites or exclusions.

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 full burden but offers minimal behavioral insight. It mentions 'deterministic conflict handling' which hints at behavior, but doesn't clarify if this is destructive (likely yes for source entity), what permissions are needed, or what the output contains. For a mutation tool with zero annotation coverage, this is insufficient.

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, efficient sentence with zero wasted words. It front-loads the core action and includes essential qualifiers ('duplicate', 'deterministic conflict handling') without redundancy, making it easy to parse quickly.

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 (merging entities with conflict resolution), no annotations, and an output schema (which reduces need to describe returns), the description is moderately complete. It covers the what and how at a high level but lacks details on behavior, side effects, and parameter usage that would be needed for confident invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate but adds little parameter detail. It implies 'source_entity_id' and 'target_entity_id' parameters through context, and mentions 'attribute_conflict' handling, but doesn't explain conflict types or the default 'target_wins' behavior. This provides some meaning beyond the bare schema but leaves key gaps.

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

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

The description clearly states the action ('merge'), the resource ('duplicate source entity into a target entity'), and the method ('deterministic conflict handling'). It distinguishes from siblings like 'update_entity' or 'delete_entity' by specifying merging with conflict resolution, but doesn't explicitly differentiate from all potential alternatives like 'connect_entities'.

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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., entities must exist), when merging is appropriate (e.g., for duplicates), or what happens to the source entity post-merge. Without such context, the agent lacks clear usage direction.

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 mentions 'prune' (implying destructive deletion) and 'dry_run' (from the schema), but doesn't disclose critical behavioral traits: whether pruning is irreversible, what permissions are required, how 'entity and type' grouping works, or potential rate limits. The description adds minimal context 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 a single, efficient sentence that front-loads the core action ('prune older reasoning/log content') and key constraint. There is zero wasted verbiage, making it easy to parse quickly.

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 complexity (a destructive pruning operation with 4 parameters), no annotations, and an output schema (which might cover return values), the description is incomplete. It lacks essential details: behavioral risks, parameter semantics, and usage context. For a tool that could delete data, this is a significant 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?

Schema description coverage is 0%, so the description must compensate for all parameters. It only vaguely references 'entity and type' (hinting at 'entity_id' and 'content_types') and 'most recent records' (hinting at 'keep_latest'), but doesn't explain parameter meanings, defaults, or interactions (e.g., how 'dry_run' affects execution). With 4 undocumented parameters, this is inadequate.

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

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

The description clearly states the action ('prune older reasoning/log content') and the resource affected, with a specific constraint ('keeping the most recent records per entity and type'). It distinguishes itself from siblings like 'delete_entity' or 'archive_entity' by focusing on retention pruning rather than full deletion or archiving. However, it doesn't explicitly differentiate from all siblings (e.g., 'apply_performance_tuning' might overlap in purpose).

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 no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, timing considerations (e.g., during cleanup cycles), or compare to siblings like 'archive_entity' for long-term storage or 'delete_entity' for complete removal. Usage is implied through the action but lacks explicit 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?

With no annotations, the description carries full burden but lacks behavioral details. It doesn't disclose permissions needed, rate limits, whether it's read-only or mutative, or how results are returned (e.g., pagination). The mention of 'generic reporting' hints at read-only use but is insufficient for a tool with potential data access implications.

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, efficient sentence that front-loads the core action ('Query a named SQL view') and adds purpose ('for generic reporting and model projections') without redundancy. Every word contributes value, making it appropriately sized.

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 2 parameters with 0% schema coverage, no annotations, and an output schema present, the description is moderately complete. It covers the basic purpose but lacks details on behavioral traits and parameter usage, though the output schema may mitigate return value explanations. For a query tool with data access, more context on safety and constraints is needed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate but only partially does. It explains that 'view_name' refers to a 'named SQL view' and 'params' are for the query, adding some meaning beyond the schema's generic titles. However, it doesn't detail param formats or constraints, leaving gaps for the two parameters.

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 ('query') and resource ('named SQL view'), specifying it's for 'generic reporting and model projections'. It distinguishes from siblings like 'run_read_query' by focusing on views rather than arbitrary queries, though it doesn't explicitly contrast 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?

No explicit guidance on when to use this tool versus alternatives like 'run_read_query' or 'list_views' is provided. The phrase 'generic reporting and model projections' implies a context but doesn't define boundaries or exclusions, leaving usage ambiguous.

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

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

With no annotations, the description carries full burden but lacks behavioral details. 'Rebuild' implies a write/mutation operation, but it doesn't disclose permissions needed, whether it's destructive (e.g., if it locks tables), execution time, or side effects. It mentions 'faster queries' as a benefit but not how or when this applies.

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, efficient sentence that front-loads the action ('Rebuild') and purpose. Every word contributes meaning, with no wasted text, making it highly concise and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (a maintenance operation with potential performance impact), no annotations, and an output schema (which handles return values), the description is minimally adequate. It states what the tool does but lacks details on behavior, prerequisites, or error conditions, leaving gaps for safe agent use.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description adds value by explaining the purpose without redundancy, earning a baseline 4 for clarity in a parameterless 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 action ('Rebuild') and target ('task summary materialized table'), with the benefit ('for faster open task queries') providing context. It distinguishes from siblings like 'get_entity' or 'run_read_query' by focusing on table maintenance rather than data retrieval or entity operations, though it doesn't explicitly name alternatives.

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?

No guidance is provided on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., when performance issues arise), exclusions, or related tools like 'apply_performance_tuning' or 'get_database_health' that might inform its 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?

No annotations are provided, so the description carries the full burden. It mentions rendering for human-readable output but doesn't disclose behavioral traits like whether it's read-only, destructive, requires permissions, rate limits, or what the output looks like. For a tool with 3 parameters and no annotation coverage, this is a significant 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 a single, well-structured sentence that efficiently conveys the core purpose and usage condition. It's front-loaded with the main action and includes no unnecessary information, making it highly concise.

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 3 parameters with 0% schema coverage, no annotations, but an output schema exists, the description is incomplete. It covers usage guidelines well but lacks parameter semantics and behavioral details. The output schema mitigates some gaps, but overall it's only minimally adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the schema provides no parameter details. The description doesn't mention any parameters or their meanings, failing to compensate for the lack of schema documentation. With 3 undocumented parameters, this is inadequate.

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 ('Render') and resource ('markdown document views'), specifying it's for 'human-readable artifact' creation. However, it doesn't distinguish this tool from 'export_markdown_views' or 'list_views' among siblings, missing explicit 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 explicitly states when to use this tool: 'only after an explicit user request for a human-readable artifact.' This provides clear context and a prerequisite, guiding the agent to invoke it only when specifically requested by the user.

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 of behavioral disclosure. It hints at a lookup operation ('resolve') but lacks details on permissions, rate limits, error handling, or what 'when possible' means (e.g., partial matches, case sensitivity). This is inadequate for a tool with 3 parameters and no annotation coverage.

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, efficient sentence with no wasted words. It's front-loaded and appropriately sized for its content, though it could benefit from more detail given the tool's complexity.

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 3 parameters with 0% schema coverage, no annotations, and an output schema (which reduces need to explain returns), the description is incomplete. It covers the basic purpose but lacks usage, behavioral, and parameter details needed for effective tool selection and invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate but adds no parameter information. It doesn't explain 'name' (e.g., format, uniqueness), 'entity_type' (e.g., allowed values, null handling), or 'limit' (e.g., pagination, default behavior). This leaves all 3 parameters poorly documented.

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

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

The description states the tool's purpose as resolving a human-readable name to an existing entity, which is clear but vague. It specifies the action ('resolve') and target ('entity'), but doesn't differentiate from siblings like 'get_entity' or 'search_content', nor does it explain what 'resolve' entails (e.g., lookup, validation, or retrieval).

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 no guidance on when to use this tool versus alternatives. It mentions 'when possible' but doesn't specify conditions, prerequisites, or exclusions, and offers no comparison to sibling tools like 'get_entity' or 'find_similar_entities' for similar tasks.

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 that queries are 'constrained' and 'read-only', which is useful behavioral context. However, it doesn't cover critical aspects like permissions needed, rate limits, error handling, or what 'constrained' entails (e.g., query complexity limits). This leaves significant gaps for a tool that executes SQL queries.

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, efficient sentence with no wasted words. It front-loads key information ('run a constrained read-only SQL query') and uses clear, structured phrasing. Every part earns its place by specifying scope and use cases.

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 complexity of a SQL query tool with 3 parameters, 0% schema coverage, no annotations, but an output schema exists, the description is partially complete. It covers the core purpose and constraints but misses parameter details and behavioral traits. The output schema reduces the need to explain return values, but more context on usage and limitations is warranted.

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%, so the description must compensate for undocumented parameters. It mentions 'SQL query' which hints at the 'sql' parameter, but doesn't explain 'parameters' (for query placeholders) or 'limit' (for result pagination). The description adds minimal meaning beyond the schema, failing to address the coverage gap adequately.

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 with specific verbs ('run', 'constrained', 'read-only') and resource ('SQL query'), and distinguishes it from siblings by specifying it's for 'diagnostics, analytics, and ad hoc retrieval'. However, it doesn't explicitly differentiate from similar tools like 'query_view' or 'search_content', which might also involve querying.

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 some context by mentioning use cases ('diagnostics, analytics, and ad hoc retrieval'), but it lacks explicit guidance on when to use this tool versus alternatives like 'query_view' or 'search_content'. No exclusions or prerequisites are stated, leaving the agent to infer usage.

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

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

With no annotations provided, the description carries full burden but only mentions FTS5 usage without detailing behavioral traits like permissions, rate limits, or what happens when FTS5 is unavailable. It lacks critical information for a search operation, such as result format 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?

The description is a single, efficient sentence with no wasted words, making it appropriately sized and front-loaded for quick understanding.

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 has an output schema, the description doesn't need to explain return values, but it's incomplete for a search tool with no annotations and low schema coverage. It mentions FTS5 but lacks details on search scope or limitations, making it minimally adequate but with clear gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate but adds no meaning beyond the schema. It doesn't explain what 'query' should contain (e.g., keywords, phrases) or how 'limit' affects results, leaving parameters semantically unclear.

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

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

The description states the tool searches 'narrative content' using 'FTS5 when available', which provides a general purpose but lacks specificity about what 'narrative content' refers to or how it differs from sibling tools like 'find_similar_entities' or 'query_view'. It's vague rather than tautological, as it doesn't merely restate the name.

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?

No guidance is provided on when to use this tool versus alternatives such as 'find_similar_entities' or 'query_view'. The description mentions FTS5 availability but doesn't explain implications or exclusions, leaving usage context unclear.

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 mentions 'Return server metadata and schema settings', which implies a read-only operation, but doesn't disclose behavioral traits like whether it requires authentication, has rate limits, or what specific metadata is included. This is a significant gap for a tool with no annotation coverage.

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, efficient sentence: 'Return server metadata and schema settings.' It is front-loaded with the core action and resource, with no wasted words, making it highly concise and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has no parameters, an output schema exists, and no annotations, the description is minimally adequate. It states what the tool returns but lacks details on the scope of 'metadata and schema settings' or behavioral context. With an output schema, it doesn't need to explain return values, but more context on usage or behavior would improve completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has 0 parameters, and the input schema has 100% description coverage (though empty). The description doesn't need to add parameter details, so it appropriately focuses on the tool's purpose. A baseline of 4 is given as it compensates well for the lack of parameters by being clear about what the tool does.

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 'Return' and the resource 'server metadata and schema settings', making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'get_database_health' or 'get_architecture_summary', which might also return server-related information, so it doesn't reach the highest score.

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 no guidance on when to use this tool versus alternatives. With sibling tools such as 'get_database_health' and 'get_architecture_summary' that might overlap in providing server-related data, there's no indication of context, prerequisites, or exclusions, leaving usage unclear.

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 full burden of behavioral disclosure. It mentions switching context and reconnecting the database, which implies a state-changing operation, but lacks details on permissions needed, side effects (e.g., impact on existing connections), error handling, or response format. This is inadequate for a mutation tool with zero annotation coverage.

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, efficient sentence that front-loads the core action and effect. Every word earns its place, with no redundancy or unnecessary elaboration, making it highly concise and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given a mutation tool with no annotations, 0% schema coverage, but an output schema exists, the description is partially complete. It covers the basic purpose but lacks details on behavior, parameters, and usage context. The output schema mitigates the need to explain return values, but overall completeness is minimal due to the gaps in guidance and transparency.

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%, so the description must compensate. It implies 'project_root' is used to switch context, adding some meaning beyond the bare schema, but doesn't explain the parameter's format, constraints, or examples. This provides marginal value, meeting the baseline for low coverage without fully addressing the gap.

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

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

The description clearly states the action ('Switch project context') and the resources affected ('root + db + export path'), with the additional effect 'reconnect database'. It specifies a concrete verb and scope, though it doesn't explicitly differentiate from sibling tools like 'get_project_context' or 'bootstrap_project_memory', which keeps it from a perfect score.

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?

No explicit guidance on when to use this tool versus alternatives is provided. The description implies usage for changing project settings but doesn't mention prerequisites, when-not scenarios, or direct comparisons to siblings such as 'get_project_context' for retrieval or 'bootstrap_project_memory' for initialization.

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 of behavioral disclosure. It mentions 'Set or merge' which hints at mutation behavior, but fails to describe permissions needed, whether changes are destructive, rate limits, or what the output looks like. This is inadequate for a mutation tool with zero annotation coverage.

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, efficient sentence with zero waste. It's front-loaded and appropriately sized for the tool's complexity, making it easy to parse quickly.

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 a mutation tool with no annotations, 0% schema coverage, but an output schema exists (which handles return values), the description is incomplete. It covers the basic purpose but lacks behavioral details and parameter explanations, making it minimally adequate but with clear gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate for undocumented parameters. It mentions 'tags' and implies 'entity_id' through 'for an entity', but doesn't explain the 'replace' parameter's role in merging vs. replacing tags. This adds minimal value beyond the schema's property names.

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

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

The description clearly states the action ('Set or merge') and resource ('tag labels for an entity'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'upsert_attributes' or 'update_entity' which might also handle tags, so it lacks sibling distinction.

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?

No guidance is provided on when to use this tool versus alternatives such as 'upsert_attributes' or 'update_entity' for tag management. The description implies usage for setting tags but doesn't specify contexts, prerequisites, or exclusions.

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 mentions 'Update' which implies mutation, but fails to disclose critical behavioral traits such as required permissions, whether changes are reversible, error handling, or rate limits. This leaves significant gaps in understanding the tool's operational impact.

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, efficient sentence that is front-loaded with the core action. There's no wasted text, though it could benefit from slightly more detail given the tool's complexity. It's appropriately sized but under-specified rather than concise.

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 mutation nature, 4 parameters with 0% schema coverage, no annotations, and multiple sibling alternatives, the description is incomplete. While an output schema exists, the description lacks context on usage, parameters, and behavioral transparency needed for safe and effective tool invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate but adds no parameter information. It doesn't explain what 'entity_id' refers to, what 'top-level state' includes, or the semantics of 'name', 'description', and 'status' parameters. This leaves all 4 parameters poorly understood beyond their basic types.

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

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

The description states the tool 'Update[s] an existing entity's top-level state' which provides a clear verb ('Update') and resource ('entity'), but it's vague about what 'top-level state' specifically means. It doesn't distinguish this tool from sibling tools like 'upsert_entity' or 'set_tags', leaving ambiguity about when to choose one over another.

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?

No guidance is provided on when to use this tool versus alternatives. With sibling tools like 'upsert_entity', 'set_tags', and 'merge_entities' available, the description offers no context about prerequisites, appropriate scenarios, or exclusions, leaving the agent to guess based on tool names alone.

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 of behavioral disclosure. It mentions 'Create or update' which implies mutation, but fails to detail permissions, side effects, rate limits, or response behavior. This is inadequate for a mutation tool with zero annotation coverage, as critical behavioral traits are omitted.

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, efficient sentence that front-loads the core action ('Create or update') without unnecessary words. It is appropriately sized for the tool's complexity, with zero wasted content, making it highly concise and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has an output schema (which handles return values), no annotations, and 3 parameters with 0% schema coverage, the description is minimally complete. It states the purpose but lacks behavioral details and parameter explanations, making it adequate only because the output schema reduces the need for return value description, but gaps remain in usage and transparency.

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%, so the description must compensate for undocumented parameters. It mentions 'flexible metadata' which hints at the 'attributes' parameter, but does not explain 'entity_id' or 'replace'. The description adds minimal meaning beyond the schema, partially addressing one of three parameters, resulting in a baseline score due to incomplete compensation.

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

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

The description clearly states the verb ('Create or update') and resource ('flexible metadata for an entity'), making the purpose specific and understandable. However, it does not explicitly differentiate this tool from similar siblings like 'set_tags' or 'update_entity', which might also handle metadata or entity modifications, leaving some ambiguity in sibling distinction.

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 no guidance on when to use this tool versus alternatives. With siblings like 'set_tags', 'update_entity', and 'upsert_entity' available, there is no indication of scenarios, prerequisites, or exclusions for choosing 'upsert_attributes', leaving the agent without usage 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?

With no annotations provided, the description carries full burden but offers minimal behavioral insight. It mentions creation and merging but doesn't disclose critical details like required permissions, whether operations are atomic/reversible, error handling, or how merging works with parameters like 'replace_attributes'. The description doesn't contradict annotations (none exist), but fails to compensate for their absence.

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, efficient sentence that front-loads the core functionality. Every word earns its place with no redundancy or fluff, making it easy to parse quickly.

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 complex mutation tool with 9 parameters, 0% schema coverage, no annotations, and multiple sibling alternatives, the description is insufficient. While an output schema exists (reducing need to describe returns), the description lacks guidance on usage, parameter meanings, and behavioral traits needed for safe and effective invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate but adds no parameter-specific information. It doesn't explain what 'entity_id', 'entity_type', or other parameters mean, how 'replace_attributes' affects merging, or the purpose of nullable fields. With 9 parameters completely undocumented in both schema and description, this is inadequate.

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 ('create' or 'merge updates') and resource ('entity'), making the purpose understandable. However, it doesn't differentiate from sibling tools like 'create_entity', 'update_entity', or 'get_or_create_entity', which would require explicit comparison to achieve a perfect score.

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 implies usage based on entity existence ('if missing' vs 'existing'), but provides no explicit guidance on when to choose this tool over alternatives like 'create_entity', 'update_entity', or 'get_or_create_entity'. No prerequisites, exclusions, or comparative context are mentioned.

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 mentions 'append|replace semantics' which gives some behavioral context about mutation modes, but doesn't disclose critical information like required permissions, whether this creates new entities or modifies existing ones, error conditions, or what the unified API entails. For a write operation with zero annotation coverage, this is insufficient.

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, efficient sentence with zero wasted words. It's front-loaded with the core purpose and includes the key behavioral detail about append/replace semantics. Every element 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?

Given 4 required parameters with 0% schema coverage, no annotations, but an output schema exists, the description provides basic purpose and mode information. However, for a write operation that likely creates or modifies content, it should explain more about the entity_id relationship, content_type options, and behavioral implications. The output schema reduces but doesn't eliminate the need for more context.

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%, so all 4 parameters are undocumented in the schema. The description mentions 'append|replace semantics' which hints at the 'mode' parameter's purpose, and 'narrative content' relates to 'content' and 'content_type', but doesn't explain 'entity_id' or provide details about parameter formats, constraints, or relationships. It adds some meaning but doesn't adequately compensate for the complete schema coverage gap.

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

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

The description clearly states the action ('Write narrative content') and resource ('content'), specifying it's done via a unified API with append/replace semantics. It distinguishes from obvious siblings like 'append_content' by mentioning both modes, but doesn't explicitly differentiate from all potential alternatives like 'update_entity' or 'upsert_entity'.

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 mentions 'append|replace semantics' which implies usage context, but provides no explicit guidance on when to use this tool versus alternatives like 'append_content' (which appears to be a sibling), 'update_entity', or 'create_entity'. There are no when-not-to-use statements or clear prerequisites.

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