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What tools exist here, and when to reach for each

emem_tools
Read-onlyIdempotent

Find the right emem tool for any task. Browse or search the full catalog by topic or data shape, see one-line triggers, and pull exact schemas to call tools directly.

Instructions

The map of emem's tool surface, and the only tool you need to find the rest. Returns the working loop in the order you walk it (name a thing, ground it, cite it, resolve it, verify it, check for drift), then every other tool grouped by the question it answers, each with its one-line trigger. Pass name to get one tool's full input schema and a runnable example, so you can use a tool without loading all of the descriptors into context. This endpoint advertises the core loop only; the Earth-observation, search, embedding and log tools are catalogued here and remain callable by name.

When to use: Call this FIRST when you do not know which emem tool answers the question, or when you need a capability you cannot see in your tool list. This responder advertises a small core loop by default rather than its full catalog, so a tool being absent from your list does not mean it is absent from the server. Pass q to search by topic (ndvi, cloud, flood, verify), name for one tool's exact schema, or no arguments for the whole map. If you want the full catalog registered as callable tools instead, reconnect to the /mcp/full endpoint; for a one-shot answer without picking a primitive at all, use emem_ask.

Example arguments: {"q":"ndvi"}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoFree-text filter over tool names, titles and trigger text, e.g. `ndvi`, `cloud`, `flood`, `verify`, `token`. Plain lowercased substring over name + title + description + trigger text, not fuzzy and not stemmed: `ndvi` hits, `vegetation index` only hits tools that spell that phrase. Combines with `shape`/`bundle`/`category`/`tier` as AND, so an over-narrow combination answers with an empty catalog rather than an error.
nameNoReturn the full descriptor for exactly this tool (input schema, runnable example, annotations), e.g. `emem_ndvi`. Use this when you already know the name and want its schema without loading the whole catalog. It SHORT-CIRCUITS: when `name` is set every other argument here is ignored, so `{name, q}` is not a search within one tool. A name this responder does not carry is not an error status, you get a body with `did_you_mean` holding up to five names that share a substring with what you asked for.
tierNoWhich slice to list. Defaults to `all`, so this tool shows the whole surface even when the endpoint advertises only the core loop, and an `extended` tool you find here is callable by name through tools/call whether or not your host listed it. Pass `core` to see only what a default connection advertises.
shapeNoFilter by what the answer looks like, which is usually the real question. `scalar` is one number at one address; `raster` is a gridded field over an area; `timeseries` is a value per timestep; `vector` is a learned embedding; `identity` is a canonical name for a thing; `token` is a citation handle; `proof` checks one.
bundleNoFilter by the job you are doing. Call with no arguments first to see each bundle and its size.
categoryNoFilter to one category. This is about the shape of the job, NOT about safety: 13 tools outside `write` declare `readOnlyHint: false` because reading a cold address can materialise or mint as a side effect, so `category: "read"` is not a safe-tools filter. Read each result's `annotations.readOnlyHint` for that.
Behavior5/5

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

Beyond the annotations (readOnlyHint=true, idempotentHint=true), the description discloses key behavioral traits: the endpoint advertises only the core loop by default, but extended tools remain callable by name; passing 'name' short-circuits and ignores other arguments; unknown names yield a 'did_you_mean' body rather than an error; and a tool being absent from the host's list does not mean it is absent from the server. These are critical runtime behaviors that annotations do not convey.

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 longer than most but every sentence contributes to understanding the tool's purpose, usage, or behavior. It is well-structured with a clear opening, a 'When to use' section, and an example. There is slight redundancy with schema examples (e.g., q examples appear both places), but overall it is appropriately sized for a tool of this complexity.

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's complexity (6 params, no output schema), the description is complete: it explains the return format (the working loop, grouped tools with one-line triggers), covers the 'name' lookup return (schema + example), and handles edge cases like unknown names (did_you_mean). It also clarifies open-world behavior (absent from list ≠ absent from server). This fully equips an agent to invoke the tool correctly.

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 coverage is 100%, so the baseline is 3. The description adds a usage-oriented summary over the raw schema: it explains how to use 'q' for topic search, 'name' to fetch one tool's schema, and no arguments for the whole map. It also gives an example argument ('{"q":"ndvi"}'). While much of this is already in the schema descriptions, the description ties them together with the discovery context, adding marginal value beyond the 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 clearly identifies the tool as a catalog/map of other tools: 'The map of emem's tool surface, and the only tool you need to find the rest.' It explains what it returns (the working loop, then every other tool grouped by question) and distinguishes itself from siblings like emem_ask, which gives a one-shot answer, and the /mcp/full endpoint. This is a specific verb+resource and differentiates from other discovery/introspection tools.

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

Usage Guidelines5/5

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

The description gives explicit when-to-use guidance: 'Call this FIRST when you do not know which emem tool answers the question, or when you need a capability you cannot see in your tool list.' It also names alternatives: 'For a one-shot answer without picking a primitive at all, use emem_ask' and 'If you want the full catalog registered as callable tools instead, reconnect to the /mcp/full endpoint.' This clearly covers when to use this tool vs alternatives.

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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