MindSync MCP

Server Quality Checklist

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Latest release: v1.4.0
Disambiguation4/5
Most tools address distinct resources and workflows: context sync, memory sessions, event bus, delegation, job lifecycle, and model/role discovery are all recognizable groups. Minor overlap exists between memory_bootstrap, get_sync_context, and pull_truth, but the descriptions provide enough distinction for an agent to pick the right tool in most cases.
Naming Consistency4/5
The set mostly follows a predictable verb_noun or role_noun snake_case pattern, such as publish_event, list_agents, delegate_task, and job_wait. A few outliers like memory_bootstrap, job_status, and health break the strict verb-first style, but the overall naming remains consistent and easy to navigate.
Tool Count3/5
With 24 tools, the server is in the heavy range and bundles many subdomains: memory, offline sync, an event bus, task delegation, job management, sessions, roles, and models. While each tool appears purposeful, the surface could feel overwhelming or be better split into separate focused MCP servers.
Completeness4/5
The main workflows offer strong lifecycle coverage: sessions have start/checkpoint/end, delegation has routing, preview, policy, agents, jobs, cancellation, wait, review, and output retrieval, and events have publish, poll, and subscribe. The main gaps are minor, such as no explicit unsubscribe, no durable fact read/delete surface, and no overall job listing tool.
Average 3.1/5 across 24 of 24 tools scored. Lowest: 2.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 54 commits in the last 12 weeks
- Last stable release on August 21, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
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If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose behavioral traits like read-only nature, error handling, or required permissions. It fails to inform the agent about side effects or constraints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short sentence, which is concise but under-specified. It does not waste words but fails to provide necessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having an output schema, the description does not mention return values or structure. The tool is simple but the description is too minimal to be considered complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description adds no meaning to the two parameters (job_id, agent_name) beyond their names. The agent must infer their purpose.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'check' and resource 'status of a delegated job', making the tool's purpose clear. However, it does not distinguish it from sibling tools like job_cancel, which also deals with jobs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 job_cancel or health. The description lacks context for when checking status is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must fully disclose behavioral traits, but it only states the basic operation. It does not mention side effects, permission requirements, idempotency, or whether the event is fire-and-forget or synchronous.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence with no wasted words. However, it lacks structure such as parameter highlights or usage context that would improve utility without sacrificing brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 4 parameters (including a nested object payload) and an output schema, the description is severely under-detailed. It fails to explain the payload structure, event type format, or the behavior of correlation_id.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning the description provides no additional meaning for any of the 4 parameters. The parameter names and types are present in the schema, but the description adds nothing about their purpose or constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Publish') and the target resource ('event to the AgentRelay Event Bus'), but it does not contrast with sibling tools like 'poll_events' or 'delegate_task', so the purpose is clear but lacks differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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, nor are any prerequisites or exclusions mentioned. The agent is left to infer usage from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It indicates flushing (likely destructive to the queue) and optional consolidation, but does not explain side effects (e.g., deletion of local queue, network requirements, 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.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is single-sentence and concise, but it sacrifices completeness. It could be expanded without losing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema (not shown) and 2 parameters with 0% schema coverage, the description is far from complete. It fails to explain agent_name, consolidation details, or behavioral implications, leaving significant gaps for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%. The description only hints at the 'consolidate' parameter ('optionally consolidate'), but does not mention the required 'agent_name' parameter or explain its purpose. This is inadequate for tool usability.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it flushes the offline queue to remote, with optional consolidation and pulling truth. It distinguishes from siblings like pull_truth (which likely pulls without flushing) and queue_durable_fact (which enqueues). However, the phrase 'consolidate + pull compiled truth' is somewhat vague.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 pull_truth or queue_durable_fact. No prerequisites or exclusions mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. Description does not disclose side effects, idempotency, rate limits, or error behavior. Minimal beyond 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.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence with 12 words, very concise and front-loaded. However, lacks essential details, making it under-specified rather than optimally concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a polling tool with 3 parameters and an output schema, the description fails to clarify behavior like pagination, blocking, or failure modes. Output schema may help but description is incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%. Only 'since_seq' is hinted at in the description; 'limit' and 'agent_name' have no semantic explanation. The description adds negligible value over the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Poll events'), resource ('Event Bus'), and key parameter ('since_seq'). It distinguishes from siblings like publish_event or job_status.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like pull_truth or sync_offline_facts. Missing context about polling frequency, blocking behavior, or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present. The description only says 'subscribe' without explaining subscription behavior (e.g., persistence, overwrite, side effects). Critical behavioral traits are missing.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, front-loaded. However, it is too brief and could achieve more with minimal length. It is not verbose but under-informative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with 2 parameters and an output schema, but the description lacks details on subscription lifecycle, response format, or side effects. It leaves significant gaps for an agent relying on the description alone.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, yet the description fails to add meaning to the parameters. It mentions 'specific event types' but doesn't clarify agent_name or event_types, nor their roles or constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The name 'subscribe_events' and description clearly state the verb 'subscribe' and resource 'event types' on the Event Bus. It distinguishes from sibling tools like publish_event and poll_events by focusing on subscription.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 (e.g., poll_events). No context, prerequisites, or exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It only states destructive action without detailing side effects, reversibility, required permissions, or error cases.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely concise single sentence with no fluff. However, brevity comes at cost of completeness, but conciseness is rated on efficiency, which is high.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite presence of output schema, description does not mention return values. Lacking parameter descriptions and behavioral details, it is incomplete for a destructive tool with two parameters.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% description coverage for both parameters. Description adds no information about job_id or agent_name, leaving the agent without guidance for correct values.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses specific verb 'Cancel' and resource 'running job', and adds 'kill its process tree' for precision. It clearly distinguishes from sibling tools like job_status.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives, no prerequisites or exclusions, and no mention that the job must be running.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full responsibility. It partially discloses the warning behavior but omits other important aspects such as irreversibility, permission requirements, error states, and what happens to existing focus data.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that front-loads the primary action. However, it is somewhat under-specified; conciseness is achieved at the expense of completeness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 5 parameters (4 required) and no parameter descriptions, the description is far too sparse. While an output schema exists, the lack of parameter semantics and behavioral details leaves the agent underinformed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema contains 5 parameters with 0% schema description coverage, and the description provides no explanations for any parameter (e.g., what 'focus' is, structure of 'paths', constraints on 'agent_name'). An agent would have to guess parameter meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Update this agent's focus') and the specific resource, including a unique behavior ('warn on overlaps with other non-stale agents'). This distinguishes it from sibling tools, which are mostly concerned with facts, events, and jobs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to use this tool versus alternatives, nor are any preconditions or exclusion criteria mentioned. An agent must infer usage from the name and description alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the burden of behavioral disclosure. It indicates a read operation ('fetch') but does not mention any side effects, error conditions (e.g., if job not completed), or required permissions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence, which is efficient but lacks meaningful detail. It is front-loaded but overly terse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite low complexity and an existing output schema, the description omits crucial context about parameter meanings, usage conditions, and behavioral traits. It is incomplete for an agent to use effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the tool description provides no explanation of the two parameters (job_id and agent_name). The output schema exists but is not referenced.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Fetch the result file content for a completed job,' specifying the verb (fetch) and resource (result file content) and distinguishing from siblings like job_status and job_cancel.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies that the tool is for completed jobs only, but it does not explicitly state when to use this tool versus alternatives like job_status or job_cancel. No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must fully disclose behavior. It implies a read operation (pull from remote) but does not mention side effects, error conditions, or required permissions. Adequate but not thorough.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is a single, efficient sentence with no redundancy. Every word contributes meaning, making it highly concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool simplicity (1 parameter, no nested objects, output schema present), the description provides minimal context. It does not explain what 'compiled-truth markdown' is or how it fits into the system, but it is not misleading.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%. The single required parameter 'agent_name' is not described in the description, so the tool adds no semantic value beyond the schema. This is a significant gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (pull), the resource (compiled-truth markdown), and the direction (remote to local). It is specific and distinct from sibling tools which focus on sync, health, queuing, etc., but does not explicitly differentiate.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like get_sync_context or sync_offline_facts. The description lacks any context about prerequisites or typical use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses the two modes of operation (remote write vs. local queue) and mentions later flush, but it lacks details on queue flush triggers, error handling, idempotency, or side effects. With no annotations, more transparency would be beneficial.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with one front-loaded sentence. While efficient, it sacrifices necessary detail on parameters and complex behavior, making it slightly under-specified.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (5 parameters, conditional logic, no annotations, and 0% schema coverage), the description is incomplete. It lacks parameter definitions, output details, and offline queue behavior specifics, leaving significant gaps for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description provides no information about the five parameters (agent_name, entity, attribute, text, confidence). With 0% schema description coverage, the agent has no guidance on how to fill these fields, making the tool hard to use correctly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: writing a durable fact with conditional behavior based on online status. It distinguishes from siblings like sync_offline_facts by specifying the remote vs. local queue logic, though it could contrast more explicitly.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use (when online, write remotely; when offline, queue locally), but it does not provide explicit guidance on when not to use or how it compares to alternatives like sync_offline_facts or direct writes.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. It does add some behavioral context: the session is 'tracked', 'local-memory', and an ID is returned. However, it does not disclose side effects, lifecycle expectations, or whether starting a session has persistent consequences.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded, information-dense sentence. Every word contributes to the core purpose and expected return value, with no filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Although an output schema exists, the description is incomplete for a tool with five parameters, no annotation coverage, and no usage guidance. It lacks parameter semantics and fails to explain the role of a local-memory session within the broader tool set.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description provides no meaning for any of the five parameters, including the required agent_name and project_key. The optional goal, branch, and workspace parameters are entirely unexplained, so an agent cannot infer how to populate them.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Start'), the resource ('tracked local-memory session'), and the key output ('return its session ID'). It is specific enough to be understood, though it does not explicitly distinguish itself from siblings like session_end or memory_checkpoint.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives, no mention of prerequisites, and no indication of what happens before or after starting a session. The description implies it is the entry point, but it never states that explicitly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that data is 'validated' and 'best-effort-redacted', which adds some behavioral context. However, it omits failure behavior, idempotency, overwrite semantics, and side effects, which are critical since annotations are absent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
One efficient sentence with zero fluff, action word front-loaded. Highly concise and appropriately brief.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 9 parameters, no schema descriptions, no annotations, and an output schema, the description is severely lacking. It does not explain parameter semantics, usage scenarios, or expected output, making it inadequate for an agent to call correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description must explain parameters but does not. It only implies they represent 'structured session memory' without clarifying fields like decisions, blockers, or durable_facts.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clear verb 'Save' and resource 'structured session memory' indicate the core action. It does not explicitly differentiate from sibling memory tools, but the specific phrasing suggests a distinct persistence operation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like memory_bootstrap or queue_durable_fact. The description provides no lifecycle context (e.g., when to call it) or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, behavioral transparency is carried by the prose; it does disclose worktree isolation, default agent auto-selection, post-run checks, and memory-project behavior. It does not explain the write flag, background behavior, or side effects/cost of delegation, leaving important execution behavior undisclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with a clear purpose and each sentence adds a distinct piece of parameter/behavior guidance with little fluff. It is dense and somewhat wall-of-text, but the complexity of 14 parameters justifies the length.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex tool with 14 parameters, no annotations, and zero schema property descriptions, this description is incomplete: it leaves the task prompt, write mode, effort, model, and background behavior unexplained. An agent could call the tool but would likely need external knowledge to use those parameters correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% for 14 parameters, so the description must compensate. It explains agent, required_capabilities, exclude_agents, worktree, checks, memory_project, and cwd context, but omits core parameters like prompt, write, effort, model, agent_name, and background—prompt is especially important since it is the task payload.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The opening line states a specific action ('Delegate a task') and target ('headless CLI agent or role'), so an agent can tell what the tool is for. It does not explicitly distinguish itself from siblings like route_task, but the delegated-vs-routed distinction is reasonably inferable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives conditional guidance for parameters ('Pass required_capabilities when...', 'exclude_agents to keep...') and mentions job_review for consuming checks. It does not say when to choose delegate_task over route_task or other siblings, so usage context is helpful but not alternative-focused.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the burden. 'Report' implies a read operation, and the listed outputs provide some transparency. However, it doesn't explicitly state read-only nature or potential side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence with no redundant words. Efficiently communicates the key report contents.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Although an output schema exists, the description lacks important context about parameter purpose and usage scenarios. For a simple health tool, it is incomplete, especially given the lack of annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description fails to explain the 'agent_name' parameter. Its default 'system' suggests filtering, but no such explanation is given, leaving the agent without guidance on parameter usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool reports health metrics: 'local paths, queue depth, and remote reachability.' The verb 'Report' and specific resources make the purpose unambiguous and distinct from sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like get_sync_context or queue_durable_fact. No context about prerequisites or intended scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must carry the full burden of behavioral disclosure. The verb 'List' implicitly suggests a read-only operation, and the phrase 'live binary availability' indicates a real-time snapshot. However, it does not explicitly state that it is non-mutating, nor does it disclose any potential side effects, authentication requirements, or rate limits. It is adequate but not comprehensive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, tightly worded sentence that front-loads the action and resource. Every word conveys meaningful information without redundancy or filler. It is perfectly concise for a list operation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with one optional parameter that is undocumented, the description leaves a critical gap. While the output schema (if rich) might explain return fields, the tool's purpose and parameter usage are not fully covered. The description does not explain how agent_name affects the output, nor does it mention any pagination or filtering behavior. The tool is simple enough that a richer description would easily make it complete, but this one does not.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has one optional parameter, agent_name, but the description makes no mention of it. Schema coverage is 0%, meaning the parameter is entirely undocumented. The description does not explain what agent_name is for, its possible values, or how it influences the results. This is a significant gap for a tool that only has one parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('List') and the resource ('dispatch agents'), and adds specific attributes ('live binary availability and routing capabilities') that distinguish this from sibling tools like list_models or list_roles. It is concise and unambiguous about what the tool does.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 does not mention any context, prerequisites, or scenarios where this tool is preferred, nor does it reference any other tool. The agent is left to infer its use case from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The word 'Read' implies a non-mutating operation, and there are no annotations to contradict that, but the description does not disclose return format, error behavior, or whether any side effects occur. Since no annotations are present, the description alone carries this burden.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two short sentences and gets to the point quickly. No unnecessary detail or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read operation, the description is mostly sufficient, but it omits parameter semantics and does not mention what the response contains. This leaves minor but real gaps for a caller.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has one parameter, agent_name, but the description does not explain its meaning or expected values. The title and default value provide only minimal hints, leaving the parameter underspecified.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific action ('Read automatic delegation policy') and identifies the resource, so the core purpose is clear. The phrase 'or offer it' is slightly ambiguous but does not obscure the main function.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides a usage cue ('Call when deciding whether to delegate or offer it') but does not explicitly state when not to use it or name alternative tools. Sibling tools like delegate_task and route_task exist, yet the description does not contrast with them.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full behavioral disclosure burden. It clearly communicates that the tool is non-committal (preview only, no job launch), which is a key safety trait. Yet it omits other behavioral facets such as side effects (e.g., logging, state changes), authentication needs, or any rate limiting considerations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
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 ('Preview automatic worker selection') and immediately qualifies the scope ('without launching a job'). Every word earns its place, with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Although the output schema exists, the description does not explain how the four input parameters influence worker selection or how to use this preview effectively. Given the presence of many sibling tools and no annotations, the description is too sparse to fully orient an agent, especially regarding parameter roles and expected behavior beyond the preview itself.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides zero description coverage (0%) and the tool description offers no explanation of what each parameter does. The parameters 'prompt', 'agent_name', 'exclude_agents', and 'required_capabilities' remain opaque, leaving the agent with no semantic guidance for constructing valid calls.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Preview') and resource ('automatic worker selection'), immediately clarifying the tool's function. The qualifier 'without launching a job' strongly distinguishes it from delegation tasks, such as the sibling 'delegate_task', making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'without launching a job' implies a preview-before-execution workflow, offering some contextual guidance. However, it does not explicitly name any alternative tools or state when to use this tool versus actually launching a job, leaving the routing decision partially inferred.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the output is a list of models and that it can be agent-specific or all agents, which is useful. Missing details: it doesn't specify the output format or any side effects, but for a read-only listing tool, the described behavior is likely sufficient, hence a 3.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, concise and front-loaded with the primary purpose, followed by a usage hint. No wasted words, though it could be slightly more structured with parameter details, but it is efficiently written.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
There is an output schema (not shown) which may cover return values, so the description doesn't need to explain them. However, with two parameters and no schema documentation, the description should provide enough context to choose between them. It doesn't fully, but for a simple listing function, it is minimally adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must compensate. It mentions 'given agent or all agents' but does not explain the two parameters ('agent' and 'agent_name') or how they interact. There is ambiguity between 'agent' and 'agent_name' that the description doesn't resolve, so this is a clear gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists available models and indicates it is tied to an agent or all agents. The verb is specific and the resource is clear, but it could benefit from distinguishing from sibling tools that also explore availability (e.g., list_agents), though the model-specific focus is evident.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Use this for choosing a model before delegating a task,' which gives a clear context for when to use the tool. However, it doesn't explicitly mention when not to use it or mention the sibling 'list_agents' as an alternative, but the use case is fairly specific.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses the prioritizing behavior ('open and recent sessions') but doesn't explain what happens with the budget_chars parameter, whether context is truncated, or what the return format looks like. The description provides minimal behavioral insight beyond a basic read operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that is front-loaded with the main action and resource. It is appropriately sized, though it could include a brief note on budget_chars or usage without becoming verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
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 describes the returned context) and simple parameters, the description is somewhat minimal. It doesn't explain what 'bounded' means or how budget_chars affects the result. For a tool that likely aggregates memory, more context about the scope and prioritization would help an agent invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description should compensate. The description does not mention any parameters, relying entirely on the schema. With 0% coverage, this is a gap, but the parameter names (agent_name, project_key, budget_chars) are fairly self-explanatory. The description adds no explanatory value for parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear verb ('Retrieve') and specific resource ('bounded project context'), with an explicit prioritization ('open and recent sessions'). It could be slightly more specific about what constitutes 'project context', but it is clearly distinguishable from siblings like get_sync_context or list_agents.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use this tool (when you need a bounded, prioritized view of project context) but does not explicitly state when not to use it or mention alternatives. Given the sibling tools like get_sync_context and pull_truth, there is no guidance to help an agent choose between them.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 for what is likely a mutating operation. It discloses that the session's status becomes 'completed' or 'failed' but doesn't address whether the operation is idempotent, reversible, whether it can be called multiple times, whether it affects in-flight operations, or what happens to the session's data afterward. For a tool that presumably mutates session state, 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
One sentence, front-loaded with the action, with zero extraneous words. Every word earns its place, and the entire behavioral essence is captured in eight words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool of moderate complexity (3 flat params, no nesting, no enums), the description is mostly adequate but leaves some contextual questions open. Given the tool's position among 23 siblings including session_start and memory_checkpoint, some guidance on the session lifecycle would help agents distinguish when this tool versus a sibling is appropriate. The output schema covers return values, so its omission from the description is acceptable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description was expected to compensate. It does meaningfully clarify the 'status' parameter by naming its two valid values ('completed or failed'), which aligns with and augments the schema's bare default of 'completed'. However, it provides no additional meaning for 'agent_name' or 'session_id' — such as whether session_id comes from a session_start call or how to obtain it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('mark'), a clear resource ('memory session'), and discloses the two possible outcome states ('completed or failed'). It clearly differentiates from sibling session_start by indicating this is the terminal operation, making the purpose immediately understandable without opening the schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage context is only implied: an agent can reasonably infer this is called when a session's work is finished, contrasting with session_start. However, there is no explicit guidance on when not to use it, prerequisites (e.g., must have an active session, must use a session_id from a prior call), or differentiation from related siblings like memory_checkpoint or memory_bootstrap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of disclosing behavior. It clearly states a read-only listing behavior and the mapping content, which is sufficient for a simple list tool. However, it does not explain the effect of the agent_name parameter, whether results are filtered, or any other behavioral nuances.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no filler. The core purpose is front-loaded, and the second sentence adds actionable guidance. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The output schema covers return values, and the description provides purpose plus usage guidance. However, the unexplained agent_name parameter and lack of explicit differentiation from list_agents leave meaningful gaps for a tool that should be trivially callable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description never mentions the single parameter agent_name or its default value. The agent is left to guess whether agent_name filters roles, selects a context, or has some other meaning. The description adds no semantic value for this parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('List') and resource ('configured roles') and clearly states the content: agent, model, and effort mappings. This distinguishes it from sibling list_agents, which presumably lists agents rather than roles.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives concrete usage context: prefer specifying a role over an agent name when delegating tasks so model and effort configurations apply automatically. It implies this tool is the way to discover those roles, though it does not explicitly name alternatives or state when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry behavioral disclosure. It reveals the offline-first behavior and optional remote pull. However, it does not state whether the tool modifies local state, what happens on error (beyond returning local data), or any permission or side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no filler. The first sentence states the primary action, and the second adds a critical qualifier. Every word is meaningful and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema (which presumably documents return values), the description covers the essential behavior and key constraints. It is sufficient for an agent to understand the tool's role, though terms like 'local session state' and 'compiled truth' could be clarified further.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description should compensate. It only hints at the 'refresh_remote' parameter ('Optionally pull remote truth first'), but provides no context for 'agent_name' or 'project_name'. Only one of three parameters receives any semantic value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool loads local session state and compiled truth, with optional remote pull. The verb 'load' and resources 'session state' and 'compiled truth' are specific, and the offline-first behavior distinguishes it from related tools like pull_truth.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use for offline-first scenarios ('always returns local data') but does not explicitly state when to use this tool versus siblings like pull_truth or sync_offline_facts. No guidance on when not to use or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. 'Get' implies a read operation, and it does disclose the content of the result (check results and diff summary). But it does not mention side effects, prerequisites like job completion, or what happens for jobs that have not yet produced a review.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two tightly written sentences with no filler. The main action is front-loaded, and the usage guidance is provided in the second sentence without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The output schema covers return value shape, so the description does not need to explain that. It provides the core purpose and usage sequence, but it leaves the agent_name parameter unexplained and does not state any prerequisites, making it minimally adequate rather than complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not compensate. It only mentions 'a job' generally and never explains job_id or the agent_name parameter. The agent must infer what agent_name means and how it affects the review.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb and resource: 'Get a mechanical review verdict for a job, including check results and diff summary.' It clearly distinguishes itself from the sibling job_result by naming it directly and explaining the difference in what it returns.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly instructs when to call it: 'Call this before job_result to skip reading the output of work that did not pass.' This gives clear sequencing and rationale. However, it does not explicitly address other related siblings like job_status or job_wait.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 explains that the call is pending and resumes when the job completes, fails, or is cancelled, which is a key non-obvious behavior. However, it does not clarify what happens on timeout or how timeout_seconds and poll_interval_seconds affect the wait.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded. The first sentence states the purpose, and the second provides essential usage guidance. Every sentence earns its place with no redundant or filler content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The core lifecycle and usage context are covered, and the output schema exists to explain return values. However, for a blocking operation with four parameters, the lack of parameter semantics and timeout/error edge-case behavior leaves meaningful gaps for an agent deciding how to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description needed to explain the parameters. job_id is inferable from the name and context, but agent_name, timeout_seconds, and poll_interval_seconds are not described anywhere. The description does not compensate for the schema's lack of parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb and resource: 'Wait for a background job to finish and return its review as a completion ping.' It clearly differentiates this from sibling tools like job_status and job_result by describing the blocking/notification behavior rather than a polling or result-fetch operation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The tool says exactly when to use it: 'Call this immediately after delegate_task(background=True)' and explicitly tells the agent not to use alternatives: 'instead of repeatedly polling job_status or ending the turn.' This is strong, actionable usage guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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