academyinfo-mcp

Server Quality Checklist

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Latest release: v0.3.1
Disambiguation3/5
Most tools are distinct (list_sources, list_indicators, explain_indicator, validate_source_coverage), but explore_universities overlaps with search_university and compare_universities, as it also resolves universities and returns side-by-side data. This could confuse agents about which tool to use for multi-university comparisons.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern in snake_case (list_, search_, get_, compare_, explain_, validate_, explore_). The verbs clearly indicate the action and the nouns describe the target, making the set predictable.
Tool Count5/5
With 8 tools, the server is well-scoped for a read-only academy information service. Each tool contributes a distinct function, and the count is within the ideal 3-15 range, avoiding both bloat and thinness.
Completeness4/5
The tool surface covers listing sources and indicators, searching/exploring universities, fetching metrics, comparing universities, explaining indicators, and validating coverage. Minor gaps such as a dedicated get_source tool or direct university detail retrieval are workarounds via existing tools, so overall coverage is strong for its stated purpose.
Average 3.5/5 across 8 of 8 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 60 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
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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

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations include readOnlyHint=true and openWorldHint=false, and the description does not contradict them. It adds context by stating metrics are 'verified' and 'local' and may be unavailable ('when available'), which informs partial-result behavior. However, it does not explain what happens if metrics are missing or how results are structured, so it adds moderate value beyond the annotations.
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 sentence, front-loaded with the verb 'Compare', and every word contributes meaning. 'when available' is a useful qualifier, not filler, making it highly concise and well-structured.
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 the tool's simplicity, the description leaves out critical invocation details. There is no output schema, so the description should explain what the comparison result looks like, but it doesn't. It also lacks parameter semantics and usage context, making it insufficient for an agent to invoke correctly in many situations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has two array parameters with no descriptions and 0% coverage. The description does not explain what 'indicators' or 'university_names' should contain, their format, or any constraints. The property names give a superficial hint, but the description fails to add meaningful parameter semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Compare' and the resource 'universities', with an implied scope of multiple universities. It differentiates from siblings like get_university_metrics (singular focus) and search_university (search), but the phrases 'verified local metrics' and 'when available' introduce some ambiguity about what exactly is being compared.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like get_university_metrics or explore_universities. There are no exclusions or context that would help an agent decide between comparable tools, so the usage is only implied by the name.
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 adds context about data quality ('verified'), scope ('local'), and conditional availability ('when available'), which complements the readOnlyHint annotation. It does not describe return format or error behavior, but the annotation already covers the safety profile, so the added context earns a mid-range score.
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, succinct sentence with no filler words. It is front-loaded with the core purpose and every word contributes to the meaning, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is minimal and omits critical information about the 'indicators' parameter and what 'metrics' entails. Given the absence of an output schema and 0% schema coverage, this leaves the agent without enough context for reliable invocation, especially since the tool has two parameters.
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 should compensate for unclear parameters. It hints that university_name is the subject ('one university') but provides no explanation for the 'indicators' array or its role in filtering metrics. This is a significant gap in parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (return) and the resource (verified local metrics for one university), making the tool's purpose distinct from siblings like compare_universities or search_university. However, it doesn't explicitly name alternatives or differentiate itself from list_indicators, so it falls short of a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for retrieving metrics of a single university ('for one university'), but provides no explicit guidance on when to choose this tool over siblings such as search_university or compare_universities. No exclusions or alternative recommendations are given, making the usage context only 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 annotation readOnlyHint=true already indicates a safe read operation. The description adds that it lists only indicators 'enabled by default', which is a useful filtering trait. However, it does not disclose return format, pagination, or what 'file-first' means.
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 sentence with no wasted words. The term 'v0.1' adds unnecessary verbosity and potential confusion, but overall it is concise and front-loaded with the main verb.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no output schema, the description should clarify what the response will contain. It does not explain what 'file-first indicators' are or what listing entails. The term 'file-first' is unexplained and likely confusing to an agent.
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 schema coverage is 100%. The baseline for zero parameters is 4. The description adds no parameter semantics, but none are needed.
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 'List ... indicators', which identifies the action and resource. It distinguishes from sibling 'list_sources' by focusing on indicators rather than sources. However, the terms 'v0.1' and 'file-first' are jargon that obscure the meaning.
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 like 'explain_indicator' or 'list_sources'. There is no mention of exclusions, prerequisites, or recommended use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=false, covering the core safety/behavioral profile. The description adds the 'factual' and 'local' qualifiers but does not disclose potential failure modes (e.g., unresolved queries, missing indicators) or output format specifics. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no filler. It is front-loaded with the main verb and resource, making it easy to scan. Every word contributes meaning.
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 tool with two array parameters and no output schema, the description is minimally acceptable. It gives a high-level idea of inputs and output but lacks details on query syntax, expected indicator names, or side-by-side formatting. Sibling comparisons are not addressed, and there is no guidance on alternative tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It implies the two parameters (university_queries and indicators) but does not elaborate on accepted formats, value sources (e.g., must come from list_indicators), or how 'resolve' handles ambiguous names. The parameter names are self-descriptive, but the description adds little beyond them.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear action ('Resolve universities') and a specific output ('factual side-by-side local indicator data'), which distinguishes it from listing sources and searching. However, it doesn't explicitly differentiate from the sibling 'compare_universities', so the purpose is clear but not fully unique.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to use this tool versus alternatives such as compare_universities, get_university_metrics, or search_university. There are no exclusions or contextual triggers, leaving the agent to infer appropriate usage.
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?
Annotations already declare readOnlyHint=true and openWorldHint=false, so the description carries a lower burden. It adds context about what is being validated (v0.1 source coverage, key policy, and bundled data boundaries), but it doesn't disclose what the tool returns or how validation results are presented. This is acceptable but not rich beyond the annotations.
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 sentence, extremely concise, and front-loads the action ('Validate'). Every word earns its place; there is no repetition or filler.
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 simplicity (0 params, no output schema), the description provides a basic understanding of what the tool validates. However, it doesn't explain what the user receives (e.g., a report, boolean, errors) or any prerequisites. This leaves some ambiguity about the tool's outcome, making it merely 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?
The tool has zero parameters, so the schema fully covers parameter semantics (100% coverage). The description adds no parameter-specific details, but none are needed. Baseline for zero parameters is 4, and the description doesn't detract from that.
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 a specific verb ('Validate') and resource ('source coverage, key policy, and bundled data boundaries'). It distinguishes this tool from siblings that list, search, or compare by focusing on a validation action. However, it doesn't explicitly contrast with any sibling, so it falls just short of a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to use this tool versus the related list/explore tools. The description implies usage for validation checks but doesn't state appropriate scenarios or exclusions. With multiple sibling tools, this lack of usage context makes it harder for an agent to select this tool confidently.
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?
Annotations already declare readOnlyHint=true and openWorldHint=false, so the agent knows it is a safe read operation. The description adds valuable context: 'local file-first' indicates the data source and 'without guessing ambiguous matches' explains matching behavior. However, return format and error handling are not disclosed, which prevents a higher score.
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 sentence, front-loaded with the verb 'Search', and every word adds meaning. It is concise and well-structured, with no wasted verbiage.
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 tool with one parameter, the description gives some context but leaves gaps: no output schema, no explanation of return format, and the phrase 'without guessing ambiguous matches' is ambiguous itself. It does not clarify how this tool relates to sibling tools like explore_universities or validate_source_coverage, so the context is incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has one parameter ('query') with no description, and schema_description_coverage is 0%. The description does not mention the parameter at all, leaving the agent to guess what format or semantics 'query' expects. Since the description is the only source of parameter info, this is a significant gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool searches university records, with the qualifiers 'local file-first' and 'without guessing ambiguous matches' providing some scope. It distinguishes itself from siblings like explore_universities by implying exact-match behavior, but it does not explicitly name an alternative.
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 some context ('file-first', 'without guessing ambiguous matches') that implies it is for exact local searches, but it does not explicitly say when to use it over alternatives or when not to. No exclusions are stated, and the sibling tools are not referenced.
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?
Annotations already declare readOnlyHint=true, so the safe read nature is covered. The description adds value by specifying the explanation includes source metadata and verification status, giving the agent a clearer picture of the response content without contradicting annotations.
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 with no unnecessary words. It immediately states the verb, object, and key extra qualifiers, making it highly efficient 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?
For a simple one-parameter read-only tool, the description covers the core purpose and hints at output content (source metadata, verification status). However, it omits any mention of return format, error behavior, or constraints, and does not connect to sibling tools. This is minimally adequate but leaves gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has a single 'indicator' parameter with zero description (0% schema coverage). The description only implies the parameter is the indicator to be explained, noting it is a v0.1 indicator, but does not compensate for missing schema details such as format, allowed values, or requiredness. This is minimal clarification.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'explain' and a precise resource 'v0.1 indicator', and further clarifies it includes source metadata and verification status. This clearly distinguishes it from sibling tools like list_indicators, which merely list indicators, and compare_universities or search_university, which target different objects.
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 is given on when to use this tool versus alternatives. The description only states the action without indicating conditions, prerequisites, or exclusions. The sibling tools are not referenced, leaving the agent without direct decision support.
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?
Annotations already declare readOnlyHint=true and openWorldHint=false, providing the safety profile. The description adds useful context about the data being 'bundled file-first' and version-limited to v0.1, which goes beyond the annotations.
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?
A single, front-loaded sentence that states exactly what the tool does and its scope. No filler or 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?
For a simple list tool with no parameters, no output schema, and strong annotations, the description is sufficient. It clearly identifies the resource type and version, covering the key context needed for safe invocation.
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 description doesn't need to explain any. Baseline is 4 for no-param tools, and the description appropriately focuses on the result rather than inputs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'List' with a clear resource ('bundled file-first academyinfo sources') and scope ('available in v0.1'). It distinguishes from sibling tools like list_indicators by focusing on sources rather than indicators.
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 when to use the tool (when you need to list available sources in v0.1) and provides version context. It doesn't explicitly mention alternatives or exclusions, but for a simple list tool the context is clear.
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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