mcp-llm-router
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation3/5
Some tool clusters overlap in purpose: router_chat vs agent_llm_request both make LLM calls, list_mcp_servers vs list_mcp_tools are easy to confuse, and the judge_* family shares a similar naming pattern despite targeting different phases. Most tools are distinct, but the boundaries between some are unclear without reading the full descriptions.
Naming Consistency4/5The vast majority of tools follow a verb_noun snake_case pattern (start_session, set_coding_task, judge_code_change). Exceptions like the memory_* noun-prefixed family, router_chat, and agent_llm_request deviate, but the overall convention is still fairly uniform.
Tool Count2/5At 28 tools, this server exceeds the 25-tool threshold and spans many subdomains (memory, sessions, MCP, coding workflow). Each tool appears purposeful, but the large surface area creates context and selection overhead for an agent.
Completeness4/5The server covers the full coding task lifecycle (plan, code review, testing, completion) and provides robust memory, session, and MCP management. Minor gaps like no session teardown or MCP disconnect exist, but agents can work around them.
Average 3/5 across 28 of 28 tools scored. Lowest: 1/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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MCP servers without a LICENSE cannot be installed.
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.
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Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.
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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
- Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Tool has no description.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness1/5Is the description appropriately sized, front-loaded, and free of redundancy?
Tool has no description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Tool has no description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Tool has no description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose1/5Does the description clearly state what the tool does and how it differs from similar tools?
Tool has no description.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Tool has no description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Tool has no description.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness1/5Is the description appropriately sized, front-loaded, and free of redundancy?
Tool has no description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Tool has no description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Tool has no description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose1/5Does the description clearly state what the tool does and how it differs from similar tools?
Tool has no description.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Tool has no description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Tool has no description.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness1/5Is the description appropriately sized, front-loaded, and free of redundancy?
Tool has no description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Tool has no description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Tool has no description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose1/5Does the description clearly state what the tool does and how it differs from similar tools?
Tool has no description.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Tool has no description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Tool has no description.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness1/5Is the description appropriately sized, front-loaded, and free of redundancy?
Tool has no description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Tool has no description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Tool has no description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose1/5Does the description clearly state what the tool does and how it differs from similar tools?
Tool has no description.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Tool has no description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior1/5
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. It says nothing about side effects, persistence, what 'default' vs 'session-specific' means, whether changes are reversible, or any required authentication or context. This is a significant gap for a configuration tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single clear sentence, but it is under-specified for a tool with 12 optional parameters. Conciseness is not merely brevity; here it omits essential information, making it more of an under-specification than an efficient summary.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (12 parameters, no annotations, no output schema provided) the description is wholly inadequate. It does not explain how to select default vs session-specific scope, what parameters do, or what the response will look like. The presence of an output schema partially covers return values, but all other context is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5Does 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 does not mention any of the 12 parameters (e.g., model, base_url, session_id), leaving their meanings entirely undisclosed beyond the schema's variable names and defaults.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action 'Configure' and the resource 'brain model', with a distinction between 'default' and 'session-specific'. This is specific enough to identify the tool's core function, though it does not explicitly differentiate from sibling tools like get_brain_config.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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, nor any mention of prerequisites or context. The description simply states what the tool does without indicating the appropriate scenarios, making it insufficient for an agent to choose between this and related tools like start_session or configure_memory.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must disclose behavioral traits, but it only mentions the action and embedding mechanism. It does not state whether the operation is a write, whether existing texts are replaced or appended, whether the operation is idempotent, any permission requirements, or the return/response format. This is a major transparency gap 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.
Conciseness2/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded, but it is under-specified. For a tool with five parameters and no annotations, this single sentence does not provide enough information to be considered effective. It is minimal to the point of being unhelpful.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (5 parameters, no annotations) and the existence of an output schema, the description should at least outline the indexing behavior and parameter roles. It does not, making the tool effectively unusable without relying on external knowledge or guessing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not explain the parameters at all. Beyond the implicit 'texts' and 'namespace', optional parameters like doc_ids, metadatas, and session_id are completely undocumented, leaving the agent to guess their meaning and usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool's purpose with a specific verb ('index'), a resource ('local memory store'), and a method ('using embeddings'). It clearly differentiates from sibling tools like memory_search and memory_delete, but does not explicitly contrast with configuration tools like configure_memory, so it stops short of a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage guidelines are provided. The description does not specify when this tool should be used over alternatives, nor does it mention any prerequisites, exclusions, or typical scenarios. The only implied guidance is that this is for adding texts to memory.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility for disclosing side effects and behavior. It simply restates the action without explaining what connecting entails (e.g., spawning a process, network requirements, persistence, or side effects), leaving the agent with no 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.
Conciseness3/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with no wasted words, but it is under-specified for a tool with this complexity. It is concise but not effectively structured to convey necessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity, four parameters, and the lack of annotations or parameter descriptions, this minimal description is grossly insufficient. The presence of an output schema does not compensate for the absence of any usage context or behavioral guidance.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has four parameters with 0% schema description coverage, and the description mentions none of them. It does not clarify the meaning of env, args, command, or server_name, failing to compensate for the schema's lack of detail.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the action (connect) and resource (another MCP server), making the tool's purpose understandable. However, it does not explicitly distinguish from sibling tools like list_mcp_servers or call_mcp_tool, so it falls short of 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.
Usage Guidelines2/5Does 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 prerequisites, configuration steps, or scenarios where connecting is appropriate, and sibling tools such as list_mcp_servers are not referenced.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must explain behavior on its own. It only states the action without disclosing side effects, such as whether it writes to an index, changes files, or requires specific permissions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no waste, earning high marks for efficiency. However, it errs on the side of under-specification rather than describing necessary context, so it is not perfect.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having an output schema, the description lacks essential behavioral and usage context. For a tool that presumably modifies an index, it should clarify side effects and when to invoke it. The minimalism leaves significant gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool description contains no parameter information. With 0% schema description coverage, the description does not compensate by explaining path_pattern or its default value, leaving parameter semantics entirely to the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('scan') and resource ('local codebase'), clearly stating the tool's function. It distinguishes from sibling memory tools by focusing on the codebase, 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.
Usage Guidelines1/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to use this tool versus alternatives like memory_index. There is no mention of prerequisites, use cases, or 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.
- Behavior2/5
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 does not mention that the tool likely uses an API key, consumes tokens, stores conversation history via session_id, or potentially calls external services. The one-sentence description reveals nothing about side effects or requirements beyond the obvious.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no fluff. It earns its place by stating the fundamental action clearly. However, it is so brief that it borders on under-specification, but conciseness itself is strong.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 9 parameters and no annotations, making it a complex tool. The description only states the basic function and omits explanations of optional parameters (base_url, provider, temperature, etc.) or how session_id affects behavior. While an output schema exists, it does not compensate for the missing context needed to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and the description adds no parameter explanations. The 9 parameters (model, prompt, base_url, provider, max_tokens, session_id, api_key_env, temperature, system_prompt) are not described in the text. Even though property names hint at meaning, the description provides zero added value for understanding parameter semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific action: 'Make a request to an OpenAI-compatible LLM provider.' It identifies the verb (make a request) and the resource (OpenAI-compatible LLM provider), which is distinct enough from sibling tools like router_chat or judge_code_change. It could be more specific about session handling, but the core purpose 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 Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no guidance on when to use this tool versus alternatives. Sibling tools include router_chat, start_session, and various judge_* tools, but no comparative context is provided. An agent is left to infer when a direct LLM request 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 carries the full burden of behavioral disclosure. It only implies that the server must be connected, but does not explain side effects, error cases, return format, or any security implications. This is a significant gap for a generic invocation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no waste. For a simple tool, this is appropriately concise, though it lacks necessary details, which affects other dimensions rather than this one.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having an output schema and only three parameters, the description over-relies on the schema. It does not explain how arguments should be structured, what 'connected' means operationally, or what the output represents. This is incomplete for a generic MCP invoker.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There is 0% schema description coverage and the description does not mention any of the three parameters (server_name, tool_name, arguments). The tool name provides little semantic value for a generic forwarder, so the description fails to help the agent understand how to populate these fields.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Call') and identifies a clear resource ('a tool on a connected MCP server'), which distinguishes it from siblings like list_mcp_tools and connect_mcp_server. However, it does not elaborate on what kinds of tools or the nature of the call, which keeps it from a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to use this tool versus alternatives. There is no mention of prerequisites like server connection status, or how this compares to list_mcp_tools or other related operations.
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 to rely on, the description carries the full burden of disclosing behavior. It only states the action without mentioning side effects, whether an active session is required, or what the output contains, leaving significant ambiguity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no wasted words, which is concise. However, it is so under-specified that it borders on being unhelpful, offering little more than a restatement of the tool's name.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 4 parameters and an output schema, but the description lacks crucial context such as valid kind values, required session state, or expected details structure. An agent would struggle to invoke this tool correctly based solely on the description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not explain the meaning of session_id, kind, message, or details beyond the generic action. The property names provide some clues, but the description fails to compensate for the lack of schema details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb 'log' and a resource 'event to a session', clearly conveying the core action. It distinguishes itself from sibling tools that focus on coding, memory, or management tasks, though it could be more specific about what constitutes an event.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 start_session or other session-related tools. The description lacks any context about prerequisites or suitable use cases.
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 must disclose behavioral traits. It merely says 'Retrieve' without stating safety (read-only), required permissions, potential errors, or the nature of the returned context. It lacks any depth 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no filler words. It wastes no tokens and is appropriately sized for the tool's simplicity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Although the tool has an output schema, the description fails to provide context on what 'full context' includes, any preconditions (e.g., session must exist), or how to retrieve session_id. For a simple one-parameter read tool, this minimal description is inadequate for reliable agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage, so the description should compensate for the sole parameter 'session_id'. However, the description adds no information about the parameter's format, how to obtain it, or its role in the retrieval. The agent is left with only the parameter name.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Retrieve the full context of a session' uses a specific verb and resource, clearly indicating the tool's function. It is implicitly distinguished from sibling tools like 'start_session', but it doesn't explicitly differentiate itself or elaborate on what 'full context' includes, 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.
Usage Guidelines2/5Does 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, prerequisites, or alternatives. The description does not mention how it relates to session creation or other retrieval tools, leaving the agent without contextual information for selection.
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 the full burden of behavioral disclosure. It reveals that the search uses embeddings and optional reranking, which hints at the underlying methodology, but it does not state whether the operation is read-only, what it returns, or any side effects. This leaves significant behavioral ambiguity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that gets directly to the point. It is perfectly front-loaded with the verb 'Search' and includes no filler. Every word contributes to conveying the core action and its optional features.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Although an output schema exists, which somewhat relieves the need to describe return values, the description is still inadequate for a tool with five parameters, two required and no annotations. It does not clarify the relationship between namespace and session_id, nor does it explain how the search handles different namespaces or session contexts. The description leaves too many contextual gaps for an agent to use the tool confidently.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must compensate, but it only mentions 'embeddings' and 'optional reranking.' The reranking reference is essentially the same as the rerank boolean parameter, adding no new meaning. The description fails to explain the purpose or format of via parameters such as namespace, top_k, and session_id, leaving much of the parameter semantics undocumented.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: searching memory, with a specific method (embeddings) and an optional feature (reranking). It distinguishes itself from sibling tools like memory_delete and memory_index, which are clearly different operations. However, it lacks a defined scope or any details about the memory being searched.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage guidance is provided. The description does not specify when to use this tool versus alternatives, nor does it mention any context in which this search should be invoked. It only states the action, leaving the agent without guidance on selection.
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 full burden of disclosing behavioral traits. It states the action 'start' but does not explain what starting a session entails: side effects, return values, persistence, or whether it overwrites existing sessions. This is a significant gap for a tool that likely creates state.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no unnecessary words. It immediately states the main action and the key parameters, making it highly concise and easy to parse. No filler or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 7 parameters and no annotation context, the description is too minimal. It does not explain the session lifecycle, the purpose of configuration parameters, or any relationship to sibling tools. While an output schema exists (per context signals), the description alone is insufficient for an agent to fully understand when and how to use the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does 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 two of seven parameters ('goal' and 'constraints'), adding minimal meaning by indicating that goal is central and constraints are optional. However, it leaves the other five parameters (context, task_id, metadata, brain_config, memory_settings) completely unexplained, failing to provide sufficient semantics for the parameter set.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('start') and resource ('new agent session'), clearly stating the tool's action. It implies scope by mentioning 'goal' and 'optional constraints', which distinguishes it from retrieval tools like get_session_context. However, it does not explicitly differentiate from all sibling tools, so it falls short of a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no explicit guidance on when to use this tool versus alternatives such as get_session_context or configure_brain. It is only implied that 'new' sessions are started here, but there is no mention of prerequisites, conditions, or exclusions. No exclusions or alternative tool references are given.
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 the full burden of behavioral disclosure. It only mentions optional memory and workflow guidance, but does not disclose potential side effects, dependencies on session state, or whether it may trigger other tools. This is thin for a central orchestration tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single compact sentence with no wasted words. It front-loads the key identity as 'Main brain chat tool' and keeps optional features brief, earning every word.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 9 parameters, no annotations, and a central orchestration role, a one-sentence description is insufficient. It doesn't explain the purpose of brain_override, how memory integration works, or what constitutes 'workflow guidance', leaving significant gaps for an agent to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so all parameter semantics must come from the description. The description vaguely hints at memory (use_memory, memory_namespace) and workflow (task_id), but it does not explain required parameters like session_id or message, nor does it clarify top_k, rerank, brain_override, or system_prompt. The compensation is minimal.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies the tool as the 'Main brain chat tool', establishing it as the primary conversational interface. It adds scope with 'optional memory + workflow guidance' which helps distinguish it from sibling tools like memory_search and agent_llm_request, though the verb is implicit rather than explicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'Main brain chat tool' implies it is the go-to tool for general chat, but there is no explicit guidance on when to use it versus alternatives like agent_llm_request or start_session. No when-not-to-use scenarios or exclusions are provided.
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 the full burden. 'Configure' implies a mutation, but there is no disclosure of persistence behavior, whether settings are merged or overridden, how global vs per-session scopes interact, or any validation or error behavior. This lack of context is a significant gap for a configuration tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with a clear verb, noun, and scope parenthetical. Every word earns its place; no redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
An output schema exists, so return value details are not necessary, but the description is too thin overall. It does not explain the tool's effect on memory configuration, how it relates to configure_brain, or what happens when the embedding/rerank objects are omitted. For a tool with two open-object parameters and zero annotations, more context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and the parameters are open objects. The description adds minimal value by linking 'embedding' and 'rerank' to the corresponding settings and 'per-session' to session_id, but it does not clarify the expected fields inside the embedding/rerank objects or how session_id controls scope.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Configure' and the resource 'memory embedding/rerank settings', with a scope indication of 'global or per-session'. It identifies the tool's function distinctly from memory retrieval tools, though it does not explicitly differentiate from sibling 'configure_brain'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The parenthetical '(global or per-session)' gives a hint about scope options, implying session_id usage, but the description provides no guidance on when to use this tool over configure_brain or other configuration tools. No when-not-to-use or alternatives 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?
No annotations are provided, so the description carries the full burden. It only states the basic listing action and does not disclose potential side effects (though likely none), error behavior, authentication needs, or what 'connected' implies. This is a minimal disclosure that adds little beyond the tool's name.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no filler, making it concise and easy to parse. However, it is slightly under-specified for a 0%-coverage schema, so it does not fully earn the top score.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has one parameter and an output schema (which likely lists return values), the description is a minimum viable summary. It lacks contextual details about prerequisites (e.g., server must be connected) and error cases, but for a simple listing tool, this may be acceptable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not explicitly reference the server_name parameter or add meaning beyond the schema. The phrase 'connected MCP server' hints at the parameter but does not explain requiredness, format, or how to identify valid server names. The description fails to compensate for the lack of parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function with a specific verb ('List') and resource ('tools on a connected MCP server'). It distinguishes itself from sibling tools like list_mcp_servers (which lists servers) and call_mcp_tool (which calls a tool), making its 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 Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage context is implied: use this tool when you need to see what tools are available on a particular connected MCP server. However, it does not explicitly mention alternatives (e.g., list_mcp_servers) or provide exclusions or prerequisites, leaving the agent to infer 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.
- Behavior3/5
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. It explicitly says 'Delete', which conveys destructive intent, but it does not disclose whether namespace deletion also removes contained documents, irreversibility, or any side effects. The core action is clear but additional behavioral context is missing.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence that states the action and scope efficiently. No filler or redundant information; every word contributes to understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is a destructive mutation with no annotations, and the description is minimal. It does not mention the effect of deleting a namespace on its contents, irreversibility, or any requirements. An output schema exists, so return values are covered, but for a delete operation the description lacks important safety and effect details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides no descriptions (0% coverage), but the description compensates by explaining the two modes: deleting a document (doc_id) or an entire namespace (namespace). The word 'or' clarifies the relationship between doc_id and namespace, which adds meaning beyond the raw schema structure.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Delete' and clearly identifies two resources: a document or an entire namespace. It is immediately clear what the tool does and how it distinguishes from siblings like memory_search or memory_index.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given about when to use this tool versus alternatives. It does not mention prerequisites, when deletion of a namespace is appropriate, or when other memory tools should be preferred. The description only states the action without contextual advice.
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 transparency burden. It describes the interaction style ('elicit... from the user') and the return value, but doesn't disclose any side effects, prerequisites (e.g., needing a task_id), or whether it blocks for user input. The omission of task_id in the Args section also reduces transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with a purpose statement, argument list, and return description. It is about the right length and each section adds value, though the markdown formatting is slightly irregular with inconsistent indentation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the tool's purpose, parameters (mostly), and return value, but misses the required task_id and doesn't explain how the tool integrates with the broader workflow (e.g., when to use versus raise_obstacle). The lack of annotation context also leaves the tool's behavior partially unspecified.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has no descriptions (0% coverage), so the description must explain all parameters. It provides helpful explanations for current_request, identified_gaps, specific_questions, decision_areas, options, and constraints, but completely omits task_id, which is required. This is a significant gap that could lead the agent to omit a required argument.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Elicit missing requirements and clarifications from the user when details are insufficient for implementation.' This is a specific verb+resource and distinguishes it from sibling tools like raise_obstacle or request_plan_approval, 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.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a clear usage context: 'when details are insufficient for implementation.' This tells the agent when to invoke it, but it doesn't explicitly exclude alternatives or name sibling tools, so it lacks explicit when-not guidance.
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 conveys the general behavior (presenting options and context to the user) and return value, but does not disclose important traits such as whether the call blocks execution, waits for user response, or has side effects. With no annotations, additional behavioral context would be valuable.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-organized with clear sections (Description, Args, Returns) and is concise. The only structural flaw is the nested indentation that ambiguously places decision_area and constraints under options rather than as top-level args, which slightly hurts readability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers purpose, most parameters, and return value, but misses the task_id parameter and does not clarify interaction behavior (e.g., blocking, waiting for user). This is adequate but incomplete for a tool with 6 parameters, no annotations, and an output schema that merely specifies a string.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds meaningful definitions for 5 of 6 parameters (problem, research, options, decision_area, constraints), filling the gap left by the schema's lack of descriptions. However, the task_id parameter is completely omitted, preventing a perfect score.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does 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 ('Involve the user') and resource ('resolve blockers or conflicts'). It distinguishes from siblings like raise_missing_requirements and request_plan_approval through the concept of 'obstacles', but does not explicitly name alternative tools or exclusions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context for when to use the tool: when there are blockers or conflicts that require user involvement. However, it lacks explicit when-not-to-use guidance or comparisons with alternative tools, so it earns a 4 rather than a 5.
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 transparency burden. It discloses that this is a mandatory final gate, warns that skipping causes severe token inefficiency, forbids claiming completion without receiving approval, and instructs recovering task_id via get_current_coding_task if missing. It does not explicitly discuss permissions or side effects, but the embedded output schema indicates current_task_metadata is returned after the operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-organized with clear headings and front-loads the purpose, but it is lengthy because it embeds the full TaskCompletionResult JSON schema and repeats the task_id recovery instruction in both the warning section and Notes. The redundancy and schema dump make it less concise than ideal, though the structure remains navigable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex final-validation tool, the description covers the call condition, prerequisites, argument meanings, and explicit constraints around claiming completion. The rich output schema fills in return-value semantics, so the description is sufficiently complete, though it does not spell out the post-approval workflow or rejection handling in prose.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, but the Args section provides a one-line semantic description for every parameter, e.g., task_id as 'Task UUID', requirements_met as 'Requirements satisfied', and implementation_details as 'Key implementation details'. This fully compensates for the sparse schema, though the descriptions are terse and lack examples or formatting constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with 'Final validation gate before declaring a task complete', which clearly states the tool's verb, resource, and phase. It also distinguishes this tool from sibling judges by specifying it is called when workflow_guidance.next_tool equals the tool name and by listing prior approvals from judge_coding_plan, judge_code_change, and judge_testing_implementation as prerequisites.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides an explicit call condition (`workflow_guidance.next_tool == "judge_coding_task_completion"`), lists prerequisites from sibling tools, and includes a 'Critical Tool Warning' that this tool must always be invoked at the appropriate stage. It does not explicitly state when not to use it or name an alternative for this final validation step, so it stops short of a 5.
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 states 'all configured' which clarifies scope (configuration vs. active connections) and implicitly indicates a read-only operation. However, it doesn't mention whether it includes credentials or status, but for a simple list operation this is acceptable.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no extraneous words. It front-loads the action and resource immediately, making it easy to parse and fully sufficient for the tool's simplicity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity, the lack of parameters, and the presence of an output schema, the description is complete. It fully explains what the tool does, and the sibling tools provide enough context for an agent to select it appropriately.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the baseline is 4. The description doesn't need to explain parameter meanings, and the schema is empty, which aligns perfectly. No additional semantic value is required.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'List' and clearly identifies the resource as 'all configured MCP server connections.' It distinguishes itself from sibling tools like 'connect_mcp_server' and 'list_mcp_tools' by focusing on listing configured connections rather than managing or inspecting tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance is given on when to use this tool versus alternatives such as 'list_mcp_tools' or 'connect_mcp_server.' While the purpose is clear, the description doesn't mention related tools or provide context on when this is the preferred choice, so usage is implied rather than stated.
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. It discloses behavioral traits such as the token inefficiency warning, the fact that it retrieves from conversation history, and the explicit instruction not to invent or transform the task_id. It adds meaningful context beyond a simple '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.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (Description, Warning, When to use, Args, Returns, Notes). However, the 'Critical Tool Warning' is repetitive and somewhat verbose, using dramatic language like 'severe token inefficiency' and 'extreme token loss', which could be trimmed without losing value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is complete for a tool with no parameters and no output schema. It covers purpose, usage, return values, and important notes about task_id handling. There is no missing information needed for the agent to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the description explicitly states 'Args: None', which is clear and sufficient. The baseline for zero-param tools is 4, and the description does not need to add more.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Retrieve the most recently active coding task UUID (task_id) and metadata from conversation history.' It uses a specific verb and resource, and distinguishes itself from siblings like set_coding_task.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'Use when the task_id is missing from context' and provides a 'When to use' section. It gives clear context for when to invoke the tool, though it does not explicitly mention alternatives or exclusions.
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?
No annotations are provided, so the description carries the burden. It discloses important behavioral traits: skipping causes severe token inefficiency, exact identifiers must be recovered via get_current_coding_task, HITL tools should be called for ambiguity, and plans missing library/internal reuse maps will be rejected. It also notes server auto-seeds sensible risk defaults. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is structured and front-loaded, but it embeds the entire JudgeResponse JSON schema (several hundred lines) plus duplicate notes, adding substantial redundancy beyond the separately provided output schema. It is not appropriately sized for a tool description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 14-parameter tool with workflow dependencies, the description covers prerequisites, HITL routing, parameter semantics, return schema, and rejection criteria. It also provides the trigger condition and final warning about required maps, making it complete for agent invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, but the Args section explains every parameter's purpose, requiredness/defaults, and complex object shapes such as library_plan and internal_reuse_components. It also adds conditional requirements (e.g., design_patterns_enforcement). Minor inconsistency: description marks research/research_urls optional while schema says required, and task_id required while schema has default empty.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with 'Validate a proposed plan and design against requirements, research needs, and risks,' a specific verb+resource that clearly distinguishes it from sibling tools like judge_code_change and judge_testing_implementation. The name and description align well.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states the trigger condition: 'Called when workflow_guidance.next_tool == "judge_coding_plan".' It also provides exclusions: if foundational choices are ambiguous, call raise_missing_requirements first; if a fundamental choice is being changed, call raise_obstacle. Clear when-to-use and when-not-to-use guidance.
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 provided, the description carries the full transparency burden and does disclose important behaviors: it requires real test evidence, returns approved:false when evidence is missing, and warns about token inefficiency if skipped. It does not explicitly describe side effects on task metadata, though the embedded response schema implies current_task_metadata is updated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness2/5Is the description appropriately sized, front-loaded, and free of redundancy?
The prose is organized and front-loaded, but the description is severely bloated by embedding the entire JudgeResponse JSON schema in the Returns section even though an output schema is already provided. Critical warnings and task_id guidance are also repeated across sections, making the description longer than necessary.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a high-complexity tool with 9 parameters and a rich output schema, the description is complete: it covers when to call it, prerequisites, required evidence, parameter semantics, failure behavior, and task_id recovery. The embedded output schema is redundant but does not leave the agent without needed context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The bare input schema has 0% description coverage, but the description's Args section fully compensates by explaining each parameter's meaning and providing concrete examples, such as 'Raw test runner output (required). For example, pytest/jest/mocha/go test/JUnit logs including pass/fail counts.' This adds substantial value beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and resource: 'Validate test quality, coverage, and execution results after code review is approved.' It also clearly distinguishes this from sibling judge tools by tying invocation to workflow_guidance.next_tool and to appearing after judge_code_change is approved.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives an explicit trigger condition ('Called when workflow_guidance.next_tool == "judge_testing_implementation"'), states it must be used after judge_code_change is approved, and explains when the tool will reject input (empty test_files or non-raw test_execution_results). It also warns against skipping it and tells the agent to recover task_id via get_current_coding_task if missing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full transparency burden and meets it extensively. It discloses that a missing/invalid diff will cause approved:false, that commodity re-solutions are rejected without justification, per-file coverage is required in reviewed_files, and exact task_id must be supplied. It also warns about token inefficiency and HITL handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with headings but is repetitive: the need for exact task_id appears at least three times, and the 'not tests' exclusion appears twice. The embedded JSON schema adds significant length, but the prose itself could be leaner without losing essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a complex workflow tool with no annotations and a rich output schema, the description is highly complete. It covers workflow triggering, HITL escalation, diff format enforcement, per-file review requirements, and task_id recovery. However, the omission of the user_requirements parameter and some redundancy prevent it from being fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Despite 0% schema description coverage, the description enriches four of five parameters: task_id must be exact and recoverable, code_change must be a unified Git diff (with specific markers), file_path is optional for single-file diffs, and change_description summarizes the change. However, user_requirements is entirely missing from the Args section, and task_id is labeled required even though the schema defaults it and only code_change is required.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states the tool's purpose: 'Review implementation code changes (not tests) strictly based on a unified Git diff patch.' It clearly differentiates from the sibling tool judge_testing_implementation and references the workflow trigger condition (workflow_guidance.next_tool == "judge_code_change").
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
A dedicated 'When to use' section explains to use the tool after creating/modifying implementation code, and explicitly says tests are validated via judge_testing_implementation. It also provides alternatives for ambiguous foundational choices (raise_missing_requirements) and fundamental changes (raise_obstacle), plus recovery instructions via get_current_coding_task.
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 provided, the description carries the full burden and delivers operational details: it warns about token inefficiency, mandates exact task_id usage, and notes enforced state transitions. It also clarifies the return structure, giving the agent a strong sense of behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear headings, but includes a very large embedded JSON schema under Returns that may be redundant with the actual output schema. The token-waste warnings are slightly repetitive, though the overall organization keeps it navigable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity and lack of annotations, the description is remarkably complete: it covers purpose, usage, parameters, return structure, and operational notes like task_id recovery. It provides enough workflow context for an agent to invoke it appropriately.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The Args section explicitly outlines each parameter with type, required status, and meaning (e.g., task_size enum values, state transition example, task_id for updates). Since schema coverage is 0%, this description fully compensates and adds significant semantic value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool creates or updates coding task metadata and returns workflow guidance, explicitly naming it the 'mandatory entry point for any coding work.' It distinguishes itself from siblings like get_current_coding_task by documenting the task_id recovery pattern.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly states when to use the tool ('any user request requiring logical code changes or creation of new files') and warns against skipping it. It also references get_current_coding_task as an alternative for recovering task_id, providing clear usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
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 thoroughly explains the user options (Approve/Modify/Reject), workflow state transitions, audit trails, and the elicitation system. This goes well beyond a basic mutation statement.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections (Description, When to use, Prerequisites, Args, Returns, etc.) and is appropriately front-loaded. It is long but avoids redundancy; each section earns its place for a complex human-in-the-loop tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is exceptionally complete for a tool with 9 parameters and an output schema. It explains return values, user options, workflow integration, state transitions, and prerequisites, making it fully actionable for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description compensates by listing all 9 parameters with brief definitions. While some definitions are somewhat tautological (e.g., 'research_urls: URLs from external research sources'), most add meaning beyond the raw schema, such as 'library_plan: Library selection map with purpose, selection, source'.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and resource: 'Present a completed plan to the user for approval before proceeding to AI judge validation.' This clearly states what the tool does and distinguishes it from sibling tools like judge_coding_plan, which handles AI validation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
An explicit 'When to use' section lists exact conditions ('After creating a detailed implementation plan but before calling judge_coding_plan') and prerequisites. This clearly communicates when to use the tool versus alternatives, such as judge_coding_plan or set_coding_task.
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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