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Flightmussy

FilmMap MCP server

What was filmed at a place

what_was_filmed_here

Find all productions filmed at a given place, ranked by coverage. Distinguishes movies and TV filmed on location from video games and anime set there.

Instructions

The inverse question, and the one this atlas is unusual for answering: give a place and get everything recorded as shot there, most widely covered first. Accepts a slug (preferred, e.g. "skellig-michael") or a name. Each result carries relation, because a video game or an anime attached to a place was SET there and not filmed there.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax productions (default 50)
placeYesPlace slug (preferred) or name, e.g. "durham-cathedral"

Schema Changelog

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

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior4/5

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

No annotations are provided, so the description carries the behavioral burden. It discloses result ordering (most widely covered first) and a key data-model nuance: results carry `relation` because video games/anime are SET rather than filmed at a place. This goes beyond the schema. It doesn't mention pagination or error behavior, but those are not critical for this simple read-style query.

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 compact: two sentences covering purpose, ordering, input format, and result semantics. The phrase 'the one this atlas is unusual for answering' is a minor stylistic flourish, but it doesn't reduce clarity. Information is front-loaded.

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 tool with no output schema, the description compensates by explaining output ordering and the meaning of `relation`. Combined with the schema's clear parameter documentation, an agent has enough context to invoke the tool correctly. It could mention what a typical result object looks like beyond `relation`, but that is a modest gap.

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 description coverage is 100%, so the baseline applies. The description reinforces that `place` accepts a slug (preferred) or name, but doesn't add new parameter-level meaning. This is acceptable because the schema already documents both `place` and `limit` adequately.

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 uses a specific verb + resource: give a place and get everything recorded as shot there. It also positions itself as the 'inverse question' to where_was_it_filmed, making the tool's identity and distinction clear. The addition of 'most widely covered first' and the relation caveat further specify what the tool returns.

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?

It clearly tells the agent when to use the tool: when you have a place and want productions filmed there. It also gives input guidance, noting that a slug is preferred over a name. It doesn't explicitly name sibling alternatives or exclusion criteria, though 'inverse question' strongly implies the counterpart tool.

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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