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alexyangjie

Multi Fetch MCP Server

by alexyangjie

search

Search the web and retrieve markdown-formatted results and links using the Firecrawl API. Specify a search query and limit the number of results.

Instructions

Searches the web using the Firecrawl search API and scrapes results in markdown and link formats by default.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSearch query string
limitNoMaximum number of results to return.

Implementation Reference

  • Pydantic model defining the input schema for the 'search' tool: query (required string) and limit (optional int, default 10).
    class Search(BaseModel):
        """Parameters for searching using Firecrawl search API."""
        query: Annotated[str, Field(description="Search query string")]
        limit: Annotated[int, Field(default=10, description="Maximum number of results to return.", ge=1)]
  • Registration of the 'search' tool in the list_tools() handler, with name='search', description, and Search.model_json_schema() as inputSchema.
    Tool(
        name="search",
        description="""Searches the web using the Firecrawl search API and scrapes results in markdown and link formats by default.""",
        inputSchema=Search.model_json_schema(),
    ),
    ]
  • The call_tool handler for 'search': validates arguments via Search model, calls firecrawl_client.search() with the query and options (limit, scrapeOptions with markdown+links formats), returns JSON result.
    if name == "search":
        try:
            args = Search(**arguments)
        except ValueError as e:
            raise McpError(ErrorData(code=INVALID_PARAMS, message=str(e)))
        try:
            if firecrawl_client is None:
                raise McpError(ErrorData(code=INTERNAL_ERROR, message="Firecrawl client is not initialised"))
            # Firecrawl v2: search(query, options?) with limit and scrapeOptions
            result = await firecrawl_client.search(
                args.query,
                options={"limit": args.limit, "scrapeOptions": {"formats": ["markdown", "links"]}},
            )
        except Exception as e:
            raise McpError(ErrorData(code=INTERNAL_ERROR, message=f"Failed to search via Firecrawl SDK: {e!r}"))
        try:
            json_text = result.model_dump_json()
        except AttributeError:
            json_text = json.dumps(result)
        return [TextContent(type="text", text=json_text)]
  • Registration of the 'search' prompt in the list_prompts() handler (prompt variant of the search tool).
    Prompt(
        name="search",
        description="Search the web using the Firecrawl search API",
        arguments=[
            PromptArgument(name="query", description="Search query string", required=True),
            PromptArgument(name="limit", description="Maximum number of results to return", required=False),
        ],
    ),
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden, but only mentions output format and API used. Does not disclose side effects, rate limits, or confirmation that it is read-only.

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?

Single sentence that is clear and reasonably concise. Could be slightly more terse, but avoids unnecessary words.

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?

Adequately describes function and output format for a simple tool. Missing usage guidance and behavioral context, but otherwise complete 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?

Input schema covers both parameters (query, limit) fully (100% coverage). Description adds no parameter-specific meaning beyond what schema provides, so baseline 3 applies.

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?

Clearly states it searches the web using Firecrawl and scrapes results in markdown and link formats. Implicitly differentiates from sibling tools 'fetch' and 'fetch_multi' which likely fetch specific URLs, but does not explicitly distinguish.

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 vs alternatives. Lacks any 'when to use' or 'when not to use' instructions, leaving the agent guessing.

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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