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Ask a free-text question about a place

emem_ask
Read-onlyIdempotent

Answer questions about real-world places with signed satellite and elevation data. Provides citation-backed facts, algorithm scores, and a verifiable receipt.

Instructions

Single-shot free-text answer about a real-world location, backed by signed satellite/elevation/water/built-up receipts. Forwards a place mention plus a question; runs the locate → recall → algorithm chain server-side; returns one packaged envelope.

When to use: Use when the question concerns a specific real-world place and a packaged, citation-bearing answer is preferable to manual primitive composition. Forward the user's question verbatim as q plus the location as place (free text), cell (cell64), or lat+lng. The server resolves the location, classifies the question to a topic, recalls every relevant band (auto-materializing Sentinel-2 / Sentinel-1 / Cop-DEM / JRC GSW / Overture / weather on miss), surfaces the algorithm recipes that compose those bands into named scores, and returns a single envelope with topic_routing, facts, algorithms_for_question, an optional Sentinel-2 RGB scene URL, and a caveats block (grid resolution, revisit cadence). All facts are signed by the responder; the signed receipt (and its content-addressed fact_cids) is surfaced at the envelope ROOT — response.receipt / response.fact_cids — exactly like every other primitive, and is also mirrored under facts_summary.receipt for back-compat. Set include_image: true to bundle the latest cloud-free Sentinel-2 thumbnail. Out-of-scope questions return topic_routing.matched_topic: null plus the full inventory so the caller can route elsewhere.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qYesUser's natural-language question about the place (e.g. "is this neighbourhood flood-prone").
latNoWGS-84 latitude (paired with `lng`; alternative to `place` / `cell`).
lngNoWGS-84 longitude (paired with `lat`).
cellNocell64 string (alternative to `place` — use when you have one from a prior emem_locate / emem_recall response). Provide this OR `place` OR `lat`+`lng`.
placeNoFree-text place name (e.g. "Mount Fuji", "Ashok Nagar, Ranchi"). REQUIRED unless `cell` or `lat`+`lng` is provided. Extract the noun phrase from the user's turn; the responder geocodes via OSM Nominatim.
includeNoOpt-in heavy response sections. Default response is slim (~5 KB): answer + algorithm key + fact_cids + caveats. Name specific sections to include them. Ignored when verbose=true (which includes everything).
verboseNoWhen true, return the full envelope: per-algorithm formula strings, temporal_recipe blocks, per-fact band_metadata duplicates, and the long _explanation prose. Default (since 2026-05-05) is false so the response fits MCP's 25 KB cap; the signed receipt + fact CIDs + algorithm keys + algorithms_cid are always retained. Pass true to get the full body when debugging.
include_imageNoBundle a Sentinel-2 RGB scene URL for the resolved cell. Adds ~1-2 s on first call.
Behavior5/5

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

Annotations (readOnlyHint=true, etc.) show safety. The description adds rich behavior: the server runs locate→recall→algorithm chain, returns a packaged envelope with signed receipts, slim default due to MCP cap, verbose option for debugging, and handling of out-of-scope queries. No contradiction with annotations.

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 well-structured: a lead sentence, a usage paragraph, parameter guidance, response structure details. Every sentence adds value. While slightly verbose on receipt placement, it is efficient and 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?

With 8 parameters and no output schema, the description comprehensively explains the response envelope (topic_routing, facts, algorithms, caveats), slim default vs verbose, and signed receipts. It covers edge cases and expected behavior, making it suitable for an AI agent. Minor omission: no detail on the algorithm recipes, but sufficient.

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?

Schema covers 100% of parameters with descriptions. The description adds meaningful usage context: forwards user question verbatim, describes three location alternatives and geocoding via OSM, explains the include array options, verbose default and MCP cap, and include_image effect. Adds value beyond schema.

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 explicitly states it answers free-text questions about real-world locations, with signed receipts. It clearly defines the tool's purpose and differentiates it from 'manual primitive composition' and sibli ng tools like emem_recall.

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 'When to use' paragraph gives clear guidance: use when a citation-bearing answer is preferred. It details how to pass parameters (q, place/cell/lat+lng), and notes that out-of-scope questions return null topic_routing for alternative routing. Lacks explicit exclusion of specific siblings, but provides adequate context.

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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