xingce-solver

by heihei999

Overview Schema Related Servers Score Discussions

Python

Local

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.5.2
Disambiguation4/5
Most tools have distinct purposes, but classify_question and route_xingce_question overlap slightly in routing, and compose_xingce_analysis_prompt vs compose_xingce_answer_prompt could confuse agents without careful reading. Overall, descriptions help disambiguate.
Naming Consistency5/5
All tool names follow a consistent verb_noun snake_case pattern (e.g., classify_question, get_analogy_reasoning_scaffold, solve_data_analysis). No mixed conventions.
Tool Count5/5
With 15 tools, the set is well-scoped for the domain of question routing, scaffolding, and solving. Each tool serves a clear purpose, and the count is within the 3-15 ideal range.
Completeness3/5
The set covers routing, prompt composition, and scaffolds for six reasoning types, but only two solve draft tools (data analysis and logic reasoning) exist. Missing solve tools for analogy, definition, graphic, etc., leaving agents to work around these gaps.
Average 3.6/5 across 15 of 15 tools scored. Lowest: 2.6/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 36 commits in the last 12 weeks
- Last stable release on July 12, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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.
If you are the author, simply .
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.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

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 provided, so description must disclose behavior. It only says 'Search' implying read-only, but lacks details on search algorithm, result format, or any side effects. Output schema exists but not described.
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 is very concise but omits essential information, making it underspecified rather than efficiently concise.
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?
With 3 parameters, no schema descriptions, and no annotations, the description is critically incomplete. It does not cover what a method card is, search behavior, or parameter usage, leaving 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?
Schema description coverage is 0%, yet the description adds no explanation for parameters query, module, or top_k. Their meanings, acceptable values, and defaults are not clarified.
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 verb 'Search' and resource 'method cards in the Xingce knowledge base'. It is specific and distinct from siblings like get_method_card which retrieves a single card.
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. The description does not differentiate from siblings like classify_question or get_method_card, nor does it mention suitable contexts.
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 convey behavioral traits. It only states it builds a draft, offering no information about side effects, read-only status, error behavior, or any other operational aspects.
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, front-loaded sentence that efficiently communicates the core purpose. However, it may be too terse, missing essential details that would not add excessive 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?
Given that the tool has an output schema and has sibling tools, the description is insufficient. It does not explain what the output structure is, how it interacts with other tools, or any preconditions for use.
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 does not mention 'question_text' or 'options' at all. It fails to add any meaning beyond the parameter names and types.
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 verb 'Build' and the resource 'structured data-analysis solving draft', and notably distinguishes from final answers. It differentiates from sibling tools like 'solve_logic_reasoning' and 'compose_xingce_answer_prompt' by specifying it produces a draft, not a final answer.
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 explicit guidance on when to use this tool versus alternatives. The description only notes that it builds a draft, but does not specify when to use it instead of, for example, 'classify_question' or 'solve_logic_reasoning'.
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 full burden for behavioral disclosure. It only states the tool produces a draft, not a final answer, but does not mention safety, side effects, permissions, or any other behavioral traits. This is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very short and front-loaded with the core purpose. However, it is under-specified, lacking critical details that would earn its place. A single sentence is too sparse for a tool with multiple parameters.
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 tool's complexity (2 parameters, required question_text) and the presence of an output schema, the description still fails to explain what inputs the tool expects or what the draft contains. It is not complete enough for reliable invocation.
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%, so the description must add meaning to parameters, but it does not mention 'question_text' or 'options' at all. The agent gets no guidance on what these parameters represent.
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 builds a 'structured logic-reasoning solving draft' and explicitly says it is 'not a final answer', providing a specific verb+resource. However, it does not differentiate from siblings like get_logic_analysis_scaffold, which may produce similar outputs.
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 'not a final answer' hints that this tool is for intermediate steps, but there is no explicit guidance on when to use it versus alternatives or when not to use it. Sibling tools exist that could overlap in function.
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 full weight for behavioral disclosure. It only states the return values, not whether the operation is read-only, idempotent, or requires permissions. No side effects or error handling are mentioned.
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, which is concise but lacks structure. It could benefit from breaking out return details or adding an example, but it is not overly 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, the description adequately lists the return fields. However, it omits contextual information like error handling, required state, or what happens if the method does not exist.
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 does not elaborate on the 'method_id' parameter beyond its schema title. No format, examples, or constraints are given, forcing the agent to rely solely on the parameter name.
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 verb 'Return' and the specific data items ('source files, pages, confidence, and review flag') for a given method. It distinguishes itself from sibling tools like 'get_method_card' or analysis scaffolds by focusing on source reference details.
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?
No explicit guidance on when to use or when to avoid, but the use case is implied: to retrieve source reference data for a method. It does not mention alternatives or prerequisites, leaving the agent to infer from context.
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. The description does not disclose behavioral traits such as read-only vs mutating, authorization needs, or side effects. It simply states the action without elaboration.
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 concise sentence with no extraneous words. The structure is efficient and front-loaded.
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?
Though the tool has an output schema, the description does not explain what the output contains. Given 14 sibling tools and the complexity of routing, more detail on return values or side effects is needed.
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 adds minimal value. It mentions 'question stem' but provides no format, constraints, or examples beyond the schema's property name.
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's action: 'Route a question stem to preliminary modules and method cards.' It uses a specific verb and resource, distinguishing it from siblings like route_xingce_question and search_methods.
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. The description does not indicate context, prerequisites, or when not to use it, leaving ambiguity among sibling tools.
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 full burden. It discloses that the tool is non-destructive (composes a prompt, no side effects) and specifies what it does not do. However, it does not explain the 'conservative' nature or describe the safety contract in detail, and behavioral traits like idempotency or error handling 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three concise sentences with no fluff. It front-loads the main purpose, adds detail on parameters, and ends with exclusions. Minor improvement could be structure with bullet points, but it is efficient for the content.
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 11 parameters, 0% schema coverage, no annotations, but an output schema exists (not shown). The description mentions returning an answer_prompt with constraints, but lacks detail on most input parameters and does not describe the output schema's fields. This leaves significant gaps for an agent to use 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%, yet the description only mentions module_hint and section_context, leaving 9 parameters (e.g., question_text, options, image_present, strict_mode, allow_answer, etc.) unexplained. This fails to add meaning beyond the schema for most parameters, despite the high parameter count.
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 composes a 'conservative answer prompt' for LLM-in-the-loop answering, mentions specific parameters like module_hint and section_context, and explicitly lists what it does not do (answer questions, call external LLM, select options). This provides a specific verb and resource, but does not explicitly distinguish from sibling compose tools like compose_xingce_analysis_prompt, though the 'conservative' hint differentiates somewhat.
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 indicates when to use module_hint and section_context for routing by exam section context, and states negative behavior (does not answer, call LLM, select options), implying when not to use. However, it lacks explicit guidance on when to choose this tool over siblings like compose_xingce_analysis_prompt or route_xingce_question.
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, so description carries full burden. It only hints at a read operation ('Return'), but no details on side effects, privileges, or what the response entails.
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 fluff; information is front-loaded and 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?
With output schema present, description is minimally adequate for a simple retrieval tool, but lacks context on what a 'method card' is or how it relates to sibling tools.
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 the only parameter 'method_id'. Description adds no extra meaning or format guidance beyond 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?
Description clearly states it returns a full method card by method_id, using a specific verb and resource. It distinguishes from siblings like search_methods which likely returns multiple results.
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 or alternatives. With siblings like search_methods and get_source_reference, explicit usage context would help.
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 states behavioral traits like not solving or computing, but it mentions 'route result' as input, which is not present in the input schema. This inconsistency misleads the agent about required inputs. It does not disclose side effects, authentication, or rate limits, but for a composition tool, the missing route result 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?
The description is concise at three sentences, with the main verb 'compose' front-loaded. It avoids unnecessary details and every sentence adds value.
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 of 7 parameters and an output schema, the description is incomplete. It fails to describe several key parameters and omits the 'route result' input mentioned in the text from the schema. The output schema exists but the description does not explain how the prompt_text is structured.
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 must compensate. It explains module_hint and section_context but ignores options, image_present, strict_mode, and include_scaffold_summary. Only 3 of 7 parameters are described. The description adds some meaning beyond schema but is insufficient for the agent to understand all parameters.
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 composes a structured analysis prompt from question and route result. It distinguishes itself from solving questions by explicitly stating what it does not do, and mentions the return type (prompt_text) and consumption target (LLM). This differentiates it from siblings like compose_xingce_answer_prompt.
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 mentions supporting module_hint and section_context to guide routing, giving context on when to use optional parameters. It also clarifies that the tool does not solve questions, which helps set expectations. However, it does not explicitly compare to sibling tools like classify_question or route_xingce_question, nor does it state prerequisites for the route result.
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 full burden. It discloses that the tool does not solve or answer questions, and describes return elements (module guess, confidence, recommended tool/track, reasoning signals). While it does not explicitly state read-only behavior, the nature of routing implies no side effects. This is good transparency.
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 brief (two sentences plus a short detailing of returns). It is front-loaded with the core purpose and includes necessary caveats. No wasted 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?
Given the tool complexity (6 parameters, no annotations, but has output schema), the description covers the primary purpose and behavior but lacks detailed parameter explanations. It is adequate for basic understanding but incomplete for comprehensive use.
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 description must compensate. It only mentions 'module_hint' and 'section_context' for guiding routing, leaving four other parameters (question_text, options, image_present, strict_mode) unexplained. This is insufficient for a 6-parameter tool.
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 verb 'route' and the resource 'question', specifying it routes to a module or scaffold without solving. It explicitly distinguishes from solving tools by stating 'does not answer questions or select options', which differentiates it from siblings like solve_data_analysis or solve_logic_reasoning.
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 context for usage ('Route a question to the recommended module or scaffold without solving') and states what it does not do, but it does not explicitly say when to use this tool versus alternatives like classify_question or the solve tools. No comparison or exclusion criteria are provided.
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 provided, so description carries full burden. It clearly declares the tool is read-only, provides scaffold components, and does not solve questions. It is transparent about its behavior, though it could mention error conditions or prerequisites.
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 concise sentences, front-loaded with purpose, followed by relevant details. No wasted words.
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 no parameters and an output schema exists, description is complete for a scaffold tool. It explains what is returned and constraints. Could provide more context on when to use this specific scaffold, but overall adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist, so baseline is 4. Description does not need to add meaning for parameters.
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?
Description clearly states it returns a read-only scaffold for definition judgement, listing specific components. It distinguishes from siblings by specifying 'definition judgement' and explicitly states it does not solve questions.
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?
Description implies usage is for obtaining the scaffold, and states what it does not do. However, it does not explicitly guide when to use this tool versus similar scaffold tools like get_analogy_reasoning_scaffold or provide alternative suggestions.
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. It clearly declares the tool is read-only ('Return the read-only method scaffold'), and explicitly states what it does not do: 'It does not solve questions, compute final answers, or select an option.' This is strong disclosure, though it could mention idempotency or lack of 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?
The description is extremely concise: two sentences with no wasted words. The first sentence front-loads the core purpose, and the second provides key details and exclusions. Every term adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters and the presence of an output schema (though not described), the description sufficiently conveys the tool's purpose and scope. It lists all major components of the scaffold and sets appropriate expectations about what the tool does not do.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the baseline is 4 per the rules. The description adds no parameter information, which is appropriate since there are none. The schema coverage is trivially 100%.
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 verb 'Return' and the specific resource 'read-only method scaffold for quantity relation reasoning'. It lists the included components (problem-type routing, quantity extraction guidance, etc.) and explicitly distinguishes what it does not do, effectively differentiating from sibling scaffolds like get_verbal_reasoning_scaffold.
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 does not provide explicit guidance on when to use this tool versus alternatives. While the name and content imply it is for quantity relation reasoning questions, there is no statement like 'Use this for quantity relation problems' or 'Consider get_logic_analysis_scaffold for logic tasks'. Sibling tools are not mentioned, so an agent must infer usage from context.
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 provided, so description carries full burden. It clearly states the tool is read-only and describes what it returns (checklists, verification steps, comparison guidance, uncertainty policy). It also explicitly states what it does not do, preventing misuse. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences deliver purpose, content, and non-usage in a front-loaded manner. Every word adds value; no redundancy.
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 zero parameters and presence of an output schema, the description explains the key contents of the scaffold (checklists, steps, etc.). It is sufficient for an agent to understand what the tool provides. Could be slightly more detailed about the format, but overall complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters (0 param count), so baseline is 4. Description adds meaning by explaining the return value components, which is valuable since there are no parameters to document.
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 it returns a read-only method scaffold for analogy reasoning, specifies the components (relation-type checklists, verification steps, etc.), and explicitly distinguishes from solving tools by stating it does not solve questions or select an answer. This differentiates it from sibling tools like solve_logic_reasoning and solve_data_analysis.
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 implies usage context: use for obtaining a reasoning scaffold, not for solving. It explicitly says 'does not solve questions or select an answer,' which guides agents away from using it for final answers. However, it does not provide explicit when-to-use vs. alternatives or mention 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.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully carries the burden of behavioral transparency. It explicitly declares the tool is read-only and lists what it provides and does not provide, such as not solving questions or computing answers. This gives a clear understanding of the tool's behavior and limitations.
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, consisting of two sentences that front-load the main purpose. Every sentence adds value: the first states what the tool does, and the second lists its capabilities and limitations. No unnecessary words are present.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the zero parameters and presence of an output schema, the description is complete. It clearly explains the tool's role as a scaffold for verbal reasoning, differentiates it from solving tools, and lists the content it provides. The context is sufficient for an agent to understand when and how to invoke it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, and schema coverage is 100%. The description adds meaning by explaining what the tool returns, which is beyond the schema. Following the guideline that 0 parameters yields a baseline of 4, this score is appropriate as no parameter information is needed.
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 that the tool returns a 'read-only method scaffold' specifically for verbal reasoning, and lists the provided guidance types. It also explicitly states what it does not do (solve questions, compute answers, select options), effectively distinguishing it from sibling tools that solve or answer questions.
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 the tool (for obtaining scaffolding for verbal reasoning) and when not to use it (when solving is needed), but it does not explicitly name alternative tools or provide when-not scenarios beyond stating it does not solve. The sibling tools include other scaffolds for different reasoning types, but no direct comparison is made.
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 burden. It clearly states the tool is read-only and lists its capabilities, including what it does not do. This provides sufficient transparency about behavior, and there is no contradiction 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?
The description consists of two concise sentences: the first states the main purpose, the second lists components and exclusions. It is front-loaded and contains no redundant or superfluous information.
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 zero parameters and an output schema (which is not shown but exists), the description provides a high-level overview of what the scaffold contains. It is likely sufficient for agent selection, though more detail on the output format could be helpful but is not required.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, so the baseline is 4. The description correctly adds no parameter information because there are none to document. It does not need to compensate for missing schema info.
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 explicitly states it returns a read-only scaffold for logic analysis reasoning, listing specific components like problem-type routing and constraint extraction. It clearly distinguishes from sibling scaffolds by focusing on logic analysis, and includes a negative statement about not solving questions, which adds clarity.
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 says it does not solve questions or select answers, which guides the agent to avoid using it for answer generation. However, it lacks explicit guidance on when to prefer this over other related tools like route_xingce_question or other scaffolds. The exclusion is clear but comparative use is not addressed.
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 burden. It clearly declares the tool is read-only and non-operative (no inspection or solving), which is transparent for a zero-parameter scaffold retrieval tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, concise, and front-loaded with the verb and resource. Every sentence adds value, and there is no extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has no parameters and is straightforward (read-only scaffold retrieval), the description covers its purpose and limitations completely. The existence of an output schema further supports completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has no parameters, so the baseline is 4. The description does not add parameter-specific information, but none is needed.
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 it returns a 'read-only method scaffold for graphic reasoning', specifying the resource and action. It lists specific components (visual observation order, checklists, etc.) and distinguishes from sibling scaffolds by tool name and purpose.
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 explains what the tool provides and explicitly states what it does not do ('does not inspect images, solve questions, or select an answer'), giving usage boundaries. However, it does not provide explicit guidance on when to use this tool versus the other reasoning scaffolds.
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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