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Glama

stl_diff_stop_ids

Read-onlyIdempotent

Compare two transit snapshots to identify stop IDs that were added or removed between them. Use this to detect changes in stop availability across service updates.

Instructions

Survival rate of stop_id and stop_code across a service change.

The single most consequential number in this whole tool. The app's saved- stops feature lives or dies on it: every code that does not survive a pick is a user whose saved stop silently stops working, with no error and no way for them to tell what happened.

Args: a: the earlier snapshot id or pin name. b: the later one. Run stl_snapshot_list to see what is stored.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
aYes
bYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already declare readOnly, idempotent, and non-destructive behavior, so the description adds little on side effects. It mentions the real-world impact of the result but not what the tool actually returns or how it behaves on error.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The core functional description is compact, but the motivational paragraph about the app's saved-stops feature and user impact is redundant for tool invocation. It adds emotional emphasis rather than operational guidance.

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?

With an output schema present and the parameter semantics explained, the description provides enough context for typical use. It does not detail the result format beyond 'survival rate', but the output schema likely covers that gap.

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 description gives meaningful context for both parameters: 'a' is the earlier snapshot and 'b' is the later one, with a pointer to stl_snapshot_list. This goes beyond the bare schema, though it leaves details like pin-name syntax unspecified.

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 identifies the tool's purpose: computing the survival rate of stop_id and stop_code across a service change. It is distinct from sibling diff/summary tools by focusing specifically on stop identifier persistence.

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

Usage Guidelines3/5

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

The description explains the input roles and points to stl_snapshot_list for retrieving snapshot IDs, which is useful. However, it does not explicitly state when to choose this tool over alternatives like stl_diff_summary or when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.