MCP-DBLP
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
Each tool has a clearly distinct purpose with no ambiguity: calculate_statistics processes existing results, export_bibtex handles BibTeX export, fuzzy_title_search and search provide different search methods, get_author_publications focuses on authors, and get_venue_info targets venues. The tools cover different aspects of the DBLP domain without overlap.
Naming Consistency5/5All tool names follow a consistent verb_noun pattern with snake_case: calculate_statistics, export_bibtex, fuzzy_title_search, get_author_publications, get_venue_info, and search. The naming is predictable and readable throughout the set.
Tool Count5/5With 6 tools, the count is well-scoped for a DBLP server, covering key operations like search, author/venue info, statistics, and BibTeX export. Each tool earns its place without feeling thin or bloated, suitable for typical academic workflows.
Completeness4/5The tool set provides strong coverage for core DBLP operations including search, author/venue retrieval, and data export, with minor gaps such as no direct tool for updating or deleting data (though this may be intentional for a read-heavy domain). Agents can effectively navigate publication workflows with these tools.
Average 3.9/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
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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?
No annotations are provided, so the description carries full burden. It discloses the return structure (a dictionary with specific keys) and behavioral details like how empty venues are treated. However, it doesn't mention error handling, performance aspects (e.g., for large arrays), or side effects. The description adds some context but isn't comprehensive.
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 appropriately sized and front-loaded with the purpose, followed by structured details on arguments and returns. Every sentence earns its place by clarifying inputs and outputs, though it could be slightly more concise by integrating the argument list into the flow rather than as a separate bullet.
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?
Given 1 parameter with 0% schema coverage and no output schema, the description does well by fully explaining the parameter and return values. It covers the tool's complexity adequately, though it could improve by adding usage context or error scenarios. The lack of annotations and output schema is compensated by the detailed description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate fully. It provides detailed semantics for the single parameter 'results', specifying it as an array of publication objects with required fields ('title', 'authors', 'venue', 'year'). This adds significant meaning beyond the bare schema, fully documenting the parameter's structure and expectations.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Calculate statistics from a list of publication results.' It specifies the verb ('calculate') and resource ('statistics'), but doesn't explicitly differentiate from siblings like 'search' or 'get_author_publications' which have different functions. The purpose is clear but lacks sibling comparison.
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. The description doesn't mention prerequisites (e.g., needing publication data first), exclusions, or compare to siblings like 'export_bibtex' or 'get_venue_info'. Usage is implied from the purpose but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes key behaviors like fuzzy matching, case-insensitive search, and default values for optional parameters. However, it lacks details on error handling, rate limits, authentication needs, or what happens with low similarity thresholds. The description doesn't contradict annotations, but it's incomplete for a tool with fuzzy matching and multiple parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and appropriately sized. It starts with a clear purpose statement, then lists arguments with detailed explanations, and ends with return value information. Every sentence adds value, though the return details could be slightly more concise. It's front-loaded with the core functionality.
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?
Given the tool's complexity (fuzzy matching, 4 parameters) and lack of annotations/output schema, the description does a good job of covering key aspects. It explains parameters thoroughly and outlines the return structure. However, it could benefit from more behavioral context (e.g., performance implications, error cases) to be fully complete for an agent's use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds significant value beyond the input schema, which has 0% description coverage. It explains each parameter's purpose: 'author_name' for full/partial name matching, 'similarity_threshold' as a float between 0-1 for match precision, 'max_results' for limiting output with a default, and 'include_bibtex' for including BibTeX entries. This compensates fully for the schema's lack of descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Retrieve publication details for a specific author with fuzzy matching.' It specifies the verb ('retrieve'), resource ('publication details'), and key behavior ('fuzzy matching'). However, it doesn't explicitly differentiate from sibling tools like 'fuzzy_title_search' or 'search', which might have overlapping functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'fuzzy_title_search' (for titles) or 'search' (which might be more general), nor does it specify prerequisites or exclusions. Usage is implied by the description but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses that the tool retrieves data from DBLP and notes that some fields may be empty, adding useful behavioral context about data source and completeness. However, it lacks details on error handling, rate limits, or authentication needs, which are important for a read operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with clear sections for arguments and returns, and every sentence adds value. It could be slightly more front-loaded by moving the note about DBLP earlier, but overall it's efficient with minimal waste.
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?
Given the tool's low complexity (1 parameter, no output schema, no annotations), the description is reasonably complete. It covers the purpose, parameter semantics, return fields, and data source limitations. However, it could improve by mentioning error cases or when to use alternatives, slightly reducing completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds significant meaning beyond the input schema, which has 0% coverage. It explains the 'venue_name' parameter as accepting names or abbreviations (e.g., 'ICLR'), clarifies it's required, and provides examples, fully compensating for the schema's lack of documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'retrieve' and resource 'detailed information about a publication venue,' making the purpose specific and unambiguous. It distinguishes this tool from siblings like 'get_uthor_publications' or 'search' by focusing on venue metadata rather than author data or broader searches.
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 like 'search' or 'fuzzy_title_search.' The description implies usage for venue details but lacks explicit context, prerequisites, or exclusions, leaving the agent to infer based on tool names alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses some behavioral traits like case-insensitive operators, lack of parentheses support, and default values for max_results and include_bibtex. However, it misses details like rate limits, error handling, or authentication needs, leaving gaps for a tool with 6 parameters.
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 appropriately sized and front-loaded with the core purpose, followed by a structured breakdown of arguments and returns. Every sentence adds value, though the parameter explanations could be slightly more concise.
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 search tool with 6 parameters, no annotations, and no output schema, the description is largely complete. It covers purpose, parameters with semantics, and return format. Minor gaps include lack of pagination details or explicit error cases, but it adequately supports agent usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Given 0% schema description coverage, the description fully compensates by providing detailed semantics for all 6 parameters. It explains the query format with examples, optional status, defaults, and filtering logic (e.g., 'case-insensitive substring filter for publication venues'), adding significant value beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Search DBLP for publications') and resource ('publications'), distinguishing it from siblings like 'get_author_publications' or 'get_venue_info' by focusing on boolean query-based search rather than author-specific or venue-specific lookups.
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 through the mention of 'boolean query string' and parameter details, but does not explicitly state when to use this tool versus alternatives like 'fuzzy_title_search' or 'get_author_publications'. No exclusions or clear alternatives 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the process: fetching BibTeX content from URLs, replacing citation keys, saving to a timestamped .bib file, and returning the file path. It covers key behaviors like network fetching and file creation, though it omits details like error handling 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.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured with sections for Arguments, Process, and Returns, making it easy to parse. It is appropriately sized, with each sentence adding value, though it could be slightly more concise by integrating the example more seamlessly.
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?
Given the complexity (network fetching, file creation) and lack of annotations or output schema, the description is largely complete. It explains the process, parameter usage, and return value. However, it could improve by mentioning potential errors (e.g., invalid URLs) or file format specifics.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must fully compensate. It provides detailed semantics for the single parameter 'links', including its type, requirement, format (HTML string with <a> tags), example, and how the href and link text are used. This adds significant meaning beyond the basic schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool exports BibTeX entries from HTML hyperlinks, specifying the exact verb ('export'), resource ('BibTeX entries'), and source ('collection of HTML hyperlinks'). It distinguishes from sibling tools like 'get_author_publications' or 'search' by focusing on BibTeX extraction from links rather than general searches or author-specific queries.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when BibTeX entries need to be exported from HTML links, but it does not explicitly state when to use this tool versus alternatives like 'fuzzy_title_search' or 'get_author_publications'. It provides an example input, which helps clarify context, but lacks explicit guidance on exclusions or prerequisites.
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 of behavioral disclosure. It effectively describes key behaviors: the search is case-insensitive, returns results sorted by similarity score, includes optional BibTeX entries, and applies filters for year and venue. It also specifies default values (e.g., max_results default is 10, include_bibtex default is false). However, it doesn't mention potential limitations like rate limits, error conditions, or authentication needs.
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 appropriately sized and front-loaded with the core purpose in the first sentence. The parameter explanations are structured as a bulleted list, which is clear and efficient. However, the 'Returns' statement could be integrated more seamlessly, and there's minor redundancy in specifying 'case-insensitive' for both title and venue_filter separately.
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 search tool with 7 parameters, no annotations, and no output schema, the description is largely complete. It covers the tool's purpose, all parameter semantics, and key behavioral aspects like sorting and defaults. The main gap is the lack of output details (only mentions 'publication objects' without specifying structure), but given the complexity and absence of an output schema, this is a minor shortfall.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Given the schema description coverage is 0%, the description compensates fully by providing detailed semantics for all 7 parameters. It explains each parameter's purpose, data types, requirements, defaults, and constraints (e.g., similarity_threshold range 0-1, case-insensitive matching for title and venue_filter). This adds significant value beyond the bare schema, making the parameters well-understood.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Search DBLP for publications with fuzzy title matching.' This specifies the verb ('search'), resource ('publications'), and method ('fuzzy title matching'), distinguishing it from sibling tools like 'search' (which lacks the fuzzy matching specification) and 'get_author_publications' (which focuses on authors rather than titles).
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 through the mention of 'fuzzy title matching' and the parameter explanations, suggesting it's for finding publications when the exact title isn't known. However, it doesn't explicitly state when to use this tool versus alternatives like the generic 'search' tool or 'get_author_publications', nor does it provide exclusions or prerequisites for use.
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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