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fetch

Read-only

Fetch full details for a specific item returned by search. The id encodes the item type: • 'expense:' — e.g., 'expense:42' for row 42 of the user's Expenses tab • 'report:' — the alphanumeric Firestore document id from search or list_reports, e.g., 'report:FQqDglExofsyyQv7aYy4' Always use the id exactly as returned by search or list_reports — do not invent or modify the trailing portion. Returns the full text content + metadata for the AI to cite.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesItem ID from a previous `search` or `list_reports` call. Format: 'expense:<row>' (integer row number) or 'report:<reportId>' (alphanumeric Firestore doc id, ~20 chars).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesEchoed back from the input
urlNo
textYesFull text content for the AI to cite
titleYes
metadataNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds valuable behavioral context: it warns against inventing or modifying the trailing portion of the id, explains the id format rules, and states that the tool returns full text content plus metadata for citation. This goes beyond the annotations and helps avoid misuse.

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 moderately detailed but well-structured with bullets for the id types. Every sentence serves a purpose: stating the function, explaining formats, giving caution, and indicating return content. It is not excessively verbose, though it could be slightly tighter by merging the caution with the examples.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has only one parameter, an output schema exists, and the annotations provide safety context, the description is complete. It explains how to construct/obtain the id, what the id encodes, and what the return value contains (full content + metadata). No additional context is needed for correct invocation.

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?

The schema already covers the id parameter fully (pattern and description), so the baseline is 3. The description adds further meaning by providing concrete examples for both expense and report id formats, explaining the encoding ('expense:<rowNumber>' vs 'report:<reportId>'), and reinforcing the need to use exact ids from prior calls. This additional guidance compensates beyond schema coverage.

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 clearly states the tool fetches full details for a specific item returned by `search`, using a specific verb and resource. It distinguishes itself by explaining the two id formats (expense and report) and emphasizing that the id must be used exactly as returned, which clarifies its scope as a unified fetch tool.

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 gives clear context on when to use the tool: after a `search` or `list_reports` call. It instructs the agent to use the id exactly as returned and not to modify it. However, it does not explicitly mention alternatives like `get_expense_by_id` or `get_report_details`, so it lacks explicit exclusions.

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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TDQS

A3.6/5.0
Disambiguation3/5

Most tools are explicitly scoped, but several analytics/retrieval tools overlap in purpose, such as get_spending_summary vs get_deep_analytics vs get_monthly_books_review, and generic search vs search_expenses vs search_knowledge. The detailed descriptions help, but an agent still has to carefully choose between near-equivalent options like correct_expenses vs update_expense and the three add_income variants.

Naming Consistency5/5

Tool names consistently use lower_snake_case with a recognizable verb prefix: get_*, list_*, add_*, create_*, check_*, scan_*, search_*, and whatif_*. Minor exceptions like fetch and search are still terse retrieval verbs rather than a different naming style, so the overall pattern is predictable.

Tool Count1/5

With 59 tools, this exceeds the 50+ threshold for an extreme tool count and creates a heavy selection surface for an agent. Even though ExpenseBot covers many subdomains, many get_/list_/add_ variants could be consolidated into fewer parameterized tools. The count undermines the otherwise clear naming structure.

Completeness3/5

The surface is strong for creating, reading, and updating expenses, reports, invoices, and Gmail scans, but there are notable lifecycle gaps: no delete/void tools for expenses, income, reports, or invoices, and no update tool for income. Several descriptions explicitly redirect unsupported edits to the web app, confirming that the assistant cannot complete those workflows directly.