CivMCP
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
Each tool has a clear, distinct purpose: generic SQL query, game entity lookup, adjacency bonus rules, comparison, tech/civic path, schema introspection, and server status. No apparent overlap or ambiguity.
Naming Consistency4/5The majority use a consistent 'rules_' prefix, but two tools (civ_status, civ_tuner_status) deviate with a 'civ_' prefix. This is a minor inconsistency but still understandable.
Tool Count5/5With 8 tools, the set is well-scoped—neither too sparse nor overwhelming. Each serves a distinct function relevant to querying and exploring Civilization VI game data.
Completeness4/5The tool set covers core CRUD-like operations (query, lookup, adjacency, compare, path) and system introspection (schema, status). It lacks obvious write/update operations, but given the read-only nature of the domain, it is reasonably complete.
Average 3.4/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- No commit activity data available
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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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?
With no annotations, the description carries full burden for behavioral disclosure. It states the content of the output (yield, tiles required, enabling/obsolescing tech/civic) but does not mention whether the operation is read-only, how errors are handled (e.g., unknown district), or any side effects. For a query-like tool, this is a notable gap.
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 brief and to the point, with no wasted words. The structure is a single sentence with an explanatory clause, which is efficient. It could be slightly clearer in phrasing, but overall it is appropriately concise.
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?
There is no output schema, so the description should explain the return format. It mentions the fields included (yield, tiles required, tech/civic) but does not clarify whether the result is a list, array, or object, nor does it cover edge cases like nonexistent districts or pagination. For a single-parameter tool, it is partially complete but not fully self-sufficient.
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?
The schema provides no description for the 'district' parameter, so the description must compensate. It gives example values that hint at the expected format (internal names like 'DISTRICT_THEATER') but does not clarify case sensitivity, full list of valid values, or whether display names are accepted. This adds some value but leaves ambiguity.
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 that the tool returns an adjacency bonus table for a district, with examples of district names (Campus, Harbor, DISTRICT_THEATER). It identifies the resource and the specific domain (adjacency bonuses), but it does not explicitly contrast with sibling tools like rules_query or rules_lookup, so it lacks explicit differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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. There is no mention of conditions, prerequisites, or alternative tools. The usage context is only implied by the description itself, which is insufficient for an agent to choose correctly among many 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 the full transparency burden. It does disclose the read-only nature, which is important, but it does not explain potential side effects, error behavior, or result limits. It also does not mention any permissions or rate constraints, leaving some behavioral aspects undocumented.
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 extremely concise, with the primary purpose in the first sentence and a specific, practical join example in the second. It avoids unnecessary detail and is well-structured for quick 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?
For a simple query tool with only two parameters and no annotations or output schema, the description conveys the core purpose and a useful join pattern. However, it leaves the limit parameter and output format unexplained, and does not address possible errors or large result sets. It is adequate but not fully complete for a new user.
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 schema lists sql and limit but provides no descriptions, and the tool description does not explain either parameter. The join hint is helpful for writing queries but does not clarify the meaning or constraints of sql or limit. With zero schema description coverage and no compensation, parameter semantics are largely missing.
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 runs read-only SELECT queries against the gameplay database, which is a specific action and resource. It also provides a useful join pattern for localization. However, it does not explicitly contrast with sibling tools like rules_lookup or rules_adjacency, so its distinct scope is only partially implied.
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 explicit guidance on when to use this tool versus the alternative rules_* tools. It does not mention scenarios where this tool is preferred or where other tools should be used instead. The only hint is the general SELECT capability, but that is implicit.
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 explicitly states the return content (cost, prerequisites, yields, unlocks, triggers, adjacency, description) and implies a read-only lookup operation. However, with no annotations provided, the description carries the full burden of behavioral disclosure and does not explicitly state that the tool has no side effects or what happens when no match is found.
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 concise, using two sentences to convey purpose and parameters. It front-loads the primary action and then lists the return fields and kind parameter, making it easy to scan. No redundant or filler content is present.
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 core functionality, return fields, and the kind parameter, but does not address error cases (e.g., lookup failure), edge cases, or the significance of the output beyond the field list. Without an output schema, this leaves some context missing for an agent deciding how to use the result.
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?
The schema has no descriptions for the parameters (0% coverage), but the tool description partially compensates by explaining that 'name' is an English name or type key and that 'kind' narrows the search with a list of possible values. It does not clarify the default behavior when kind is omitted or the expected format of name, leaving some ambiguity.
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: looking up a specific game entity by English name or type key, and lists the types of entities covered. It does not explicitly differentiate itself from sibling tools like rules_query or rules_adjacency, but the verb 'look up' and the return field list make the primary 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 Guidelines1/5Does 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 instead of alternatives. It does not mention situations where rules_lookup is preferable to rules_query, rules_compare, or other siblings, nor does it explain what makes this tool the right choice for a given request.
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?
The description reveals it performs a read-only check and conditional read, but does not mention possible outcomes such as errors when the socket is unreachable or behavior when no game is loaded. Since no annotations are present, this lack of detail leaves behavioral expectations unclear.
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 front-loads the primary action and avoids unnecessary detail. It is well-structured and to the point.
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?
With no output schema, the description should clarify return values or error conditions, but it does not. It leaves ambiguous what happens when the socket is unreachable or when no game is loaded, making it incomplete for an 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?
The schema has zero parameters, and the description adds no parameter information because none exist. This is acceptable as the baseline for 0 params is 4, and no further explanation is needed.
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 checks FireTuner socket reachability and reads the current turn if a game is loaded. It is specific and distinguishes itself from the sibling civ_status by focusing on the socket and turn, rather than a generic status.
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 explicit guidance is provided on when to use this tool versus alternatives like civ_status or rules_query. The description only states the action without specifying appropriate scenarios or conditions.
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 explains what the tool returns but does not explicitly state whether it is read-only or has side effects. Since no annotations are provided, the description carries the burden; it is not contradictory, but leaves some ambiguity about mutation.
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 fully conveys the core functionality without unnecessary words. It is well-structured and front-loaded with the key action.
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?
For a simple tool with one parameter, the description covers the main purpose and output content. However, it omits parameter format details and any usage context, leaving some gaps for an agent relying solely on this 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?
The single parameter 'target' is only described by its name and type (string). The description mentions 'target' but does not specify the expected format (e.g., technology ID, civic name) or provide constraints or examples.
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: it returns every technology or civic needed (transitively) to reach a given target, including costs and eras. This is specific and distinguishes it from siblings or generic queries.
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 does not provide explicit guidance on when to use this tool versus the sibling tools (e.g., rules_query, rules_lookup). The purpose is inferable, but no conditions or alternative contexts 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 of behavioral disclosure. It only states the action without mentioning side effects, permission requirements, error handling, or whether the operation is read-only. While listing is inherently non-destructive, the description does not add any behavioral context beyond the bare function.
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, well-structured sentence that front-loads the action and clearly separates the two modes (no table vs. table provided). Every word earns its place with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple schema-listing tool with no output schema, the description covers the essential purpose and parameter effect. It doesn't specify return format or edge cases, but given the low complexity and absence of output schema, it is sufficiently complete for an agent to call it correctly.
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?
The schema has 0% description coverage, so the description must compensate. It does by explaining that the 'table' parameter, when provided, lists columns instead of tables. This adds meaningful semantics beyond the raw schema (which only says 'string or null'). However, it lacks details like case sensitivity, format expectations, or examples.
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 a specific action ('List') and a specific resource ('tables in the gameplay database, or the columns of one table'). It distinguishes itself from sibling tools (rules_query, rules_lookup, etc.) which focus on querying data rather than inspecting schema. An agent can immediately understand what this tool does and when to use it.
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 description implies usage: use it to list tables, or with a table parameter to list columns. However, it does not explicitly state when to prefer this over alternatives or mention exclusions. Sibling tools like rules_query are for data retrieval, so the context is clear but no direct guidance is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must carry the burden. It indicates this is a status/diagnostic tool, implying a read-only operation, but does not explicitly state it is safe or side-effect free. It adds context about what is reported but lacks details on limitations 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence that is front-loaded and delivers the core purpose without any filler. Every word contributes to the meaning.
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?
This is a simple diagnostic tool with no parameters and no output schema. The description adequately explains what it reports (where data is looked for and what is visible). No additional information is necessary for an agent to call it 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, so the schema is fully covered. According to guidelines, a baseline of 4 applies for tools with no parameters. The description doesn't need to explain parameters, and it doesn't.
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 ('Report') and a specific resource ('CivMCP's data locations'). It clearly distinguishes this from sibling tools like rules_query or civ_tuner_status by focusing on data visibility rather than rule operations.
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 description implies it is for checking where data is located, but provides no explicit guidance on when to use it versus alternatives. There is no mention of exclusions or conditions, so usage context is only implied.
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?
Since no annotations are provided, the description carries the full burden. It implies a read-only comparison but does not explicitly state that there are no side effects or permissions required, leaving some 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 with an illustrative example, delivering the essential information without unnecessary elaboration.
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?
The description is sufficient for basic usage given the low complexity and absence of an output schema, though it does not mention return format or edge cases.
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, but the example clarifies that 'kind' identifies the category and 'a' and 'b' are the two entries to compare, adding meaningful context beyond raw parameter names.
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: comparing two entries 'side-by-side' and provides a concrete example with parameters, making it easy to distinguish from sibling tools like rules_query or rules_lookup.
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 explains when to use the tool (for two entries of the same kind) and gives a representative example, but does not explicitly contrast with sibling tools or state when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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