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BradMorphsters

tuskledger-mcp

search_transactions

Find transactions by searching partial or full words in names, merchants, and notes. Use for fuzzy recall when dates or amounts are unknown.

Instructions

Free-text search across transaction names, merchant names, and notes. Use when the user asks 'find that Whole Foods charge from last week' or 'when did I last pay Verizon?'. Different from query_transactions in that this is a fuzzy text search, not a structured filter.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qYesSearch string. Matches partial words, case-insensitive.
limitNoMax rows (default 50).

Implementation Reference

  • The actual HTTP call for search_transactions — makes a GET to /api/transactions/search with the query string and limit params.
    def search_transactions(self, q: str, limit: int = 50) -> Any:
        return self._request(
            "GET",
            "/api/transactions/search",
            params={"q": q, "limit": limit},
        )
  • Tool definition/registration in the TOOLS list — defines name, description, and JSON Schema input for search_transactions.
    Tool(
        name="search_transactions",
        description=(
            "Free-text search across transaction names, merchant names, and "
            "notes. Use when the user asks 'find that Whole Foods charge "
            "from last week' or 'when did I last pay Verizon?'. Different "
            "from query_transactions in that this is a fuzzy text search, "
            "not a structured filter."
        ),
        inputSchema={
            "type": "object",
            "required": ["q"],
            "properties": {
                "q":     {"type": "string",  "description": "Search string. Matches partial words, case-insensitive."},
                "limit": {"type": "integer", "description": "Max rows (default 50)."},
            },
            "additionalProperties": False,
        },
    ),
  • Dispatch branch that routes the 'search_transactions' tool name to the client method, extracting required 'q' and optional 'limit' from arguments.
    if name == "search_transactions":
        return client.search_transactions(q=a["q"], limit=a.get("limit", 50))
  • The Tool object itself is part of the TOOLS list which is registered via server.list_tools() returning the list.
    Tool(
        name="search_transactions",
  • Test that confirms missing required 'q' argument raises a KeyError, validating the dispatch logic.
    def test_dispatch_search_transactions_requires_q():
        client = MagicMock()
        with pytest.raises(KeyError):
            srv._dispatch("search_transactions", {}, client)
Behavior3/5

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

No annotations provided, so description carries full burden. It mentions fuzzy matching and case-insensitivity but does not explicitly state the tool is read-only or describe result behavior beyond limit parameter. Returns a list of transactions, but no detail on ordering or that it doesn't modify data.

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?

Two concise sentences front-load key purpose and usage boundaries. Every sentence adds value with zero repetition.

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 search tool with 2 parameters and no output schema, the description covers purpose, usage guidance, and search scope. Missing explicit mention that search is read-only and that results may be ordered by relevance, but overall adequate.

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 has 100% coverage of parameters (q and limit), and schema descriptions are adequate. Description adds minimal new info beyond schema (e.g., default limit 50 already in schema). No further enrichment needed.

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?

Description clearly states it performs free-text search across transaction names, merchants, and notes. Provides specific example queries ('find that Whole Foods charge', 'when did I last pay Verizon?') and explicitly distinguishes from sibling tool query_transactions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly tells when to use this tool ('free-text search') and contrasts with query_transactions ('structured filter'). Includes concrete user expressions to guide invocation.

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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