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titaniumtushar

burp-mcp-plus

dedup_search

Search a registered dedup source with regex across URL, request, response, or params. Returns match index, URL, status, and snippet. Use dedup_get for complete data.

Instructions

Regex-search a registered dedup source.

field: one of url, request, response, params, or all. Returns matches with index + url + status + a short snippet (token-thrifty). Use dedup_get to fetch the full request/response.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
patternYes
fieldNourl
limitNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The MCP tool handler for 'dedup_search'. Decorated with @mcp.tool(), it delegates to dedup.search() and returns JSON results.
    @mcp.tool()
    def dedup_search(
        name: str,
        pattern: str,
        field: str = "url",
        limit: int = 20,
    ) -> str:
        """Regex-search a registered dedup source.
    
        `field`: one of `url`, `request`, `response`, `params`, or `all`.
        Returns matches with index + url + status + a short snippet (token-thrifty).
        Use `dedup_get` to fetch the full request/response.
        """
        return json.dumps(dedup.search(name, pattern, field=field, limit=limit), indent=2)
  • The core search implementation in the dedup module. Compiles a regex, searches across the selected field(s) (url, request, response, params, or all), and returns matching entries with context snippets.
    def search(
        name: str,
        pattern: str,
        field: str = "url",
        limit: int = 20,
        flags: int = re.IGNORECASE,
    ) -> list[dict[str, object]]:
        src = get(name)
        rx = re.compile(pattern, flags)
        field = field.lower()
        out: list[dict[str, object]] = []
        for e in src.entries:
            haystacks: list[str] = []
            if field in ("url", "all"):
                haystacks.append(e.url)
            if field in ("request", "all"):
                haystacks.append(e.request)
            if field in ("response", "all"):
                haystacks.append(e.response)
            if field in ("params", "parameters", "all"):
                haystacks.append(e.parameters)
            for h in haystacks:
                m = rx.search(h)
                if m:
                    # Snippet around the match for context.
                    start = max(0, m.start() - 60)
                    end = min(len(h), m.end() + 60)
                    snippet = h[start:end].replace("\n", " ")
                    out.append({
                        "index": e.index,
                        "method": e.method,
                        "url": e.url,
                        "status": e.status,
                        "match_field": field if field != "all" else _which_field(e, h),
                        "snippet": snippet,
                    })
                    break
            if len(out) >= limit:
                break
        return out
  • The tool is registered via the @mcp.tool() decorator on line 656, which is part of the FastMCP framework.
    @mcp.tool()
    def dedup_search(
  • The DedupEntry dataclass defines the schema for each entry returned/used by dedup_search. Contains index, method, url, status, length, parameters, request, and response fields.
    @dataclass
Behavior3/5

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

With no annotations, the description carries full responsibility. It discloses the token-thrifty nature (short snippet) and regex search behavior, but lacks details on error handling, case sensitivity, or performance implications.

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 brief yet complete, using a list for field values and separate lines for clarity. Every sentence adds value without 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?

Given the existence of an output schema and siblings, the description adequately explains the tool's purpose, parameters, and return format. It could mention how to discover valid 'name' values (via dedup_list), but overall is sufficient.

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 coverage is 0%, so the description partially compensates by explaining the 'field' parameter options and implying 'pattern' is a regex. However, 'name' and 'limit' are not elaborated, leaving gaps.

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 specifies a clear verb ('Regex-search') and resource ('registered dedup source'), enumerates field options, and states the return format (index, url, status, snippet). It distinguishes itself from the sibling tool dedup_get, which fetches full data.

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 provides guidance on field values and explicitly directs to use dedup_get for full request/response, offering an alternative. However, it does not include explicit when-to-use or when-not-to-use context relative to other siblings like dedup_list.

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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