12306-mcp

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Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes (date, tickets, interline tickets, station queries by different criteria, train route). However, get-station-code-by-names and get-station-code-of-citys could be confused as both involve station codes, but descriptions clarify the input type.
Naming Consistency3/5
All tools follow get-<resource>-<detail> pattern with snake_case, but there are inconsistencies: 'citys' is a typo, and some use singular 'station' while others use plural 'stations' (e.g., get-stations-code-in-city vs get-station-code-by-names).
Tool Count4/5
8 tools cover core query needs for a train information server (date, tickets, stations, routes). Not excessive or insufficient, though a tool for train number details might be expected.
Completeness4/5
Covers essential queries: date, tickets, interline tickets, station codes by multiple methods, station details, and train routes. Missing price or order-related queries, but the scope is focused on info retrieval.
Average 3.7/5 across 8 of 8 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- 6 of 7 community issues answered or closed in the last 6 months
- 8 commits in the last 12 weeks
- Last stable release on July 30, 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.
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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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description implies a read operation but does not disclose any behavioral traits such as data freshness, error handling, or rate limits. No annotations provided to fill the gap.
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 concise but somewhat minimal; it restates the tool's function without extra value. Could be more structured but not 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?
For a simple one-parameter query tool, description is adequate. However, it lacks explanation of output format or return value, which would be helpful given no output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and already explains the 'citys' parameter format. Description adds no additional meaning beyond what the schema provides.
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?
Description clearly states the verb '查询' (query) and resource 'station_code' for Chinese city names. However, it does not differentiate from sibling tools like 'get-station-code-by-names' or 'get-stations-code-in-city'.
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. Description lacks context for appropriate usage or exclusions.
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 'query remaining tickets' without disclosing any behavioral traits like read-only nature, rate limits, or side effects. Minimal 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 a single, front-loaded sentence that efficiently states the tool's purpose with no wasted words. It is appropriately sized for the information conveyed.
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 (10 parameters, no output schema), the description is minimal. It covers the basic purpose but lacks details on return values, pagination, or limitations. However, the thorough schema descriptions partially compensate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds no parameter meaning beyond what the schema provides. The schema descriptions are detailed, but the tool description itself does not compensate.
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 queries remaining ticket info from 12306, with a specific verb and resource. However, it does not differentiate from sibling tools like get-interline-tickets, lacking explicit distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, nor does it mention prerequisites or context. The schema parameters offer some implicit guidance, but the description itself is silent.
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 provided, so the description carries full burden. It discloses that the result is a list containing station names and codes, but lacks details on error handling, performance, or side effects. Adequate for a simple query tool but not comprehensive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence in Chinese that is front-loaded and conveys the core purpose without extraneous information. Every word earns its place.
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?
For a simple tool with one parameter and no output schema, the description provides enough context: it returns a list with names and codes. Missing details like error behavior are not critical for basic usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a description for 'city'. The tool's description repeats that it uses Chinese city names, but adds no new meaning beyond the schema examples. Baseline of 3 is appropriate.
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 that the tool queries all station names and their station codes for a given Chinese city. It uses specific verb and resource, and scope 'all' is explicit. However, it does not differentiate from the sibling tool 'get-station-code-of-citys', which may cause confusion.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives like 'get-station-code-of-citys'. The usage is implied for one Chinese city, but without comparisons or exclusions, an AI agent may struggle to select correctly.
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 provided, so description carries full burden. It discloses the limitation of returning only the first ten results, but does not mention other behavioral traits like error handling, performance, or rate limits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no waste, front-loaded with purpose. Efficient and clear.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having 12 parameters and no output schema, the description is too brief. It does not explain return structure, error states, or any post-processing steps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the description adds minimal extra meaning beyond the schema. The statement about only supporting top ten conflates with limitedNum parameter but does not provide new insight into parameter usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states it queries 12306 transfer ticket information, and distinguishes itself from sibling tools like get-tickets by specifying 'interline' (中转). The limitation of top 10 results is also mentioned.
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 (for transfer tickets) but does not explicitly mention when not to use or alternatives. The sibling tool get-tickets suggests direct tickets, 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.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description only mentions returning station_code and station name. It does not disclose any behavioral traits like read-only nature or error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that efficiently conveys the tool's function without unnecessary words. It is front-loaded and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity of the tool and no output schema, the description adequately covers the input and output. However, it could mention the output structure or provide more context about the station_code format.
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 100% coverage, and the description adds value by explaining the '|' separator and giving concrete examples, which aids understanding beyond the schema's description.
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 purpose: query station_code and station name using specific Chinese station names. It distinguishes from sibling tools that use telecode or city-based queries.
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 explains how to input station names (separator '|' for multiple) but does not explicitly state when to use this tool over alternatives 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.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It describes the output (stations, times, durations) but lacks details on error handling, authentication, rate limits, or what happens if the train does not exist. The description is functional but not fully transparent about behavioral traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that efficiently conveys the tool's purpose and usage context. Every part of the sentence adds value, and there is no redundant information. It is appropriately front-loaded and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description mentions querying stops within a 'specified interval,' but the input schema does not include an interval parameter, causing a slight mismatch. Without an output schema, the description lists some return fields but omits the full structure. The tool is moderately complex with three parameters, and the description could be more complete by clarifying the interval concept.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already has 100% description coverage, so the description adds limited semantic value beyond the schema. It reinforces the context of querying stops but does not provide additional meaning for each parameter, except for the format parameter where it recommends 'text' default, which is already in the schema. Baseline score of 3 is appropriate.
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 queries detailed stop information for a specific train, including stations, arrival/departure times, and stop duration. It explicitly indicates when to use it: 'when the user asks about stops of a specific train.' This distinguishes it from sibling tools that deal with station codes or ticket purchasing.
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 provides a clear usage guideline: use this interface when the user asks about the stops of a specific train. However, it does not explicitly mention when not to use it or suggest alternatives among sibling tools, which would have improved the score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must cover behavioral traits. It describes a simple query with no side effects, but lacks details on authorization, rate limits, or error handling. Adequate but minimal.
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 compact sentences, no fluff, and the essential purpose and usage context are front-loaded. Highly concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simplicity (1 param, no output schema), the description covers purpose and context well. It could mention the return format or limitations, but overall it is sufficient for an agent to 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.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description mentions 'station_telecode' as a 3-letter code, which matches the schema, but adds no further semantic value beyond what the schema provides.
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 queries station details by telecode, including name, pinyin, and city. It differentiates from sibling tools like get-station-code-by-names by specifying the input is a telecode.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says this is for known telecodes and special/debugging purposes, and that it is rarely triggered in normal user conversations. It implies alternatives exist but does not name them.
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?
The description fully discloses the timezone (Asia/Shanghai, UTC+8), date format (yyyy-MM-dd), and primary use case. With no annotations provided, the description effectively conveys all necessary behavioral traits for a read-only, deterministic 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 extremely concise—two sentences that immediately state the purpose, timezone, format, and use case. Every sentence adds value without redundancy.
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's simplicity (no parameters, no output schema), the description covers all relevant context: timezone, format, and intended usage. Nothing is missing for an agent to correctly invoke the tool.
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 input schema is complete. The description adds no parameter-specific info, which is acceptable as there are none. Baseline 4 is appropriate.
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 the tool's function: get current date in Shanghai timezone with a specific format. It also explains its utility in parsing relative dates, making the purpose very clear.
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 provides a specific usage context (parsing relative dates like 'tomorrow', 'next Wednesday'), guiding when to use this tool. While no explicit alternatives or exclusions are given, the simplicity of the tool makes this sufficient.
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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