Fetch categorised transactions and a spending summary for a completed session. Preview sessions return 5 sample rows, locked_rows (date, category, masked description) for the rest, and unlock_url.
| Name | Required | Description | Default |
|---|---|---|---|
| session_id | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations available, the description carries the full transparency burden and does a solid job: it discloses conditional behavior for preview sessions versus completed sessions, mentions the 5-sample-row limit, locked_rows, and unlock_url. It does not cover error cases or authentication, but for a simple fetch tool this is meaningful behavioral detail.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two compact sentences: the first front-loads the core function, and the second adds the key behavioral nuance. Every phrase adds information, and there is no redundant or filler wording.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-parameter tool with no output schema, the description covers the main call contexts, including completed sessions and preview sessions, and lists the returned components. It could be more complete by specifying expected error behavior for invalid session IDs, but the essential usage context is present.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero description coverage, and the tool description does not explain the format, origin, or constraints of session_id beyond implying it identifies a session. The session concept is indirectly clarified by the completed/preview distinction, but that is too weak to compensate for a completely undocumented required parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Fetch') and resource ('categorised transactions and a spending summary for a completed session'), which makes the tool's purpose immediately clear. It also distinguishes from siblings like get_job by focusing on transaction data and session completion state, including concrete preview behavior.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear context that the tool is intended for completed sessions and separately describes what happens for preview sessions. It does not explicitly compare against convert_statement or get_job, but the session-specific wording is enough to guide typical use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool maps to a distinct stage of the workflow: converting/uploading a statement, polling job status, and fetching results. There is no meaningful overlap between the three actions.
All tools use a consistent verb_noun snake_case pattern: convert_statement, get_job, get_transactions. The naming is predictable and easy to navigate.
Three tools is an appropriate minimal set for a single-purpose conversion pipeline. Each tool is necessary and the server avoids extraneous or redundant functionality.
The core job lifecycle is fully covered: start a conversion, poll its status, and retrieve transactions. Minor gaps such as session history or cancellation exist, but they are not essential for the server's stated purpose.