Skip to main content
Glama
openags

Paper Search MCP

by openags

search_citeseerx

Search academic papers from the CiteSeerX digital library to find relevant research publications for your query.

Instructions

Search academic papers from CiteSeerX digital library.

Args: query: Search query string (e.g., 'machine learning'). max_results: Maximum number of papers to return (default: 10). Returns: List of paper metadata in dictionary format.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
max_resultsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • Tool definition and handler for 'search_citeseerx'. It uses 'async_search' with 'citeseerx_searcher'.
    async def search_citeseerx(query: str, max_results: int = 10) -> List[Dict]:
        """Search academic papers from CiteSeerX digital library.
    
        Args:
            query: Search query string (e.g., 'machine learning').
            max_results: Maximum number of papers to return (default: 10).
        Returns:
            List of paper metadata in dictionary format.
        """
        papers = await async_search(citeseerx_searcher, query, max_results)
        return papers if papers else []
  • The actual implementation of the CiteSeerX search logic, within the 'CiteSeerXSearcher' class.
    def search(self, query: str, max_results: int = 10, **kwargs) -> List[Paper]:
        """
        Search CiteSeerX for computer science papers.
    
        Args:
            query: Search query string
            max_results: Maximum results to return (default: 10)
            **kwargs: Additional parameters:
                - year: Filter by publication year
                - author: Filter by author name
                - venue: Filter by conference/journal venue
                - min_citations: Minimum citation count
                - sort: Sort by 'relevance', 'date', 'citations'
    
        Returns:
            List of Paper objects
        """
        papers = []
    
        try:
            # Prepare parameters for CiteSeerX API
            params = {
                'q': query,
                'max': min(max_results, 100),  # CiteSeerX default max
                'start': 0,
                'sort': kwargs.get('sort', 'relevance')
            }
    
            # Add filters
            if 'year' in kwargs:
                year = kwargs['year']
                if isinstance(year, str) and '-' in year:
                    # Handle year range
                    year_range = year.split('-')
                    if len(year_range) == 2:
                        params['year'] = f"{year_range[0]}-{year_range[1]}"
                else:
                    params['year'] = str(year)
    
            if 'author' in kwargs:
                params['author'] = kwargs['author']
    
            if 'venue' in kwargs:
                params['venue'] = kwargs['venue']
    
            if 'min_citations' in kwargs:
                params['minCitations'] = kwargs['min_citations']
    
            logger.debug(f"Searching CiteSeerX with params: {params}")
    
            response = self._get(self.SEARCH_API, params=params)
            response.raise_for_status()
    
            data = response.json()
    
            # CiteSeerX API returns results in 'result' field
            results = data.get('result', {}).get('hits', {}).get('hit', [])
    
            # Handle single result (API returns dict instead of list for single result)
            if isinstance(results, dict):
                results = [results]
    
            for result in results:
                try:
                    paper = self._parse_citeseerx_result(result)
                    if paper:
                        papers.append(paper)
                        if len(papers) >= max_results:
                            break
                except Exception as e:
                    logger.warning(f"Error parsing CiteSeerX result: {e}")
                    continue
    
            logger.info(f"Found {len(papers)} papers from CiteSeerX for query: {query}")
    
        except requests.RequestException as e:
            logger.error(f"CiteSeerX API request error: {e}")
            if hasattr(e, 'response') and e.response is not None:
                logger.error(f"Response status: {e.response.status_code}")
                if e.response.status_code == 429:
                    logger.warning("CiteSeerX rate limit exceeded")
        except json.JSONDecodeError as e:
            logger.error(f"Failed to parse CiteSeerX JSON response: {e}")
        except Exception as e:
            logger.error(f"Unexpected error in CiteSeerX search: {e}")
    
        return papers
  • Task registration mapping 'search_citeseerx' in the server.
    task_map[source] = search_citeseerx(query, max_results_per_source)
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 states it's a search operation and mentions the return format ('List of paper metadata in dictionary format'), but lacks critical details: whether it's read-only (implied but not explicit), rate limits, authentication needs, error handling, or what specific metadata fields are included. For a tool with no annotation coverage, this leaves significant gaps.

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 well-structured and appropriately sized. It front-loads the core purpose, then uses clear sections (Args, Returns) to detail parameters and output. Every sentence adds value, with no redundant information. Minor improvement could make it a 5, such as integrating the sections more seamlessly.

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's low complexity (2 parameters, no nested objects) and the presence of an output schema (which handles return values), the description is minimally adequate. However, with no annotations and 0% schema coverage, it should do more to explain behavioral aspects like search scope or limitations. It meets basic needs but has clear gaps in context.

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?

Schema description coverage is 0%, so the description must compensate. It adds meaningful semantics for both parameters: 'query' is explained as a 'Search query string (e.g., 'machine learning')' and 'max_results' as 'Maximum number of papers to return (default: 10).' This clarifies purpose and default behavior beyond the bare schema, though it doesn't cover constraints like query syntax or max_results limits.

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's purpose: 'Search academic papers from CiteSeerX digital library.' This specifies the verb ('search') and resource ('academic papers'), and identifies the source ('CiteSeerX digital library'). However, it doesn't explicitly differentiate this tool from its many sibling search tools (like search_arxiv, search_pubmed, etc.), which would require 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?

The description provides no guidance on when to use this tool versus alternatives. With numerous sibling tools available (e.g., search_arxiv, search_pubmed), there's no indication of when CiteSeerX is preferable, what types of papers it covers, or any prerequisites. Usage is implied only by the tool name and source mention.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/openags/paper-search-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server