define_lithologyLook up Macrostrat lithology definitions by name (e.g. "granite", "limestone") returning lith_id, name, type, group, and class; omit lith to list all lithologies (capped at 50 results).Returns lithology id, name, type, group, and class. Keyless.
| Name | Required | Description | Default |
|---|---|---|---|
| lith | No | Lithology name to match, e.g. "granite". Omit to list all. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "lith": "granite"
+ },
+ {
+ "lith": "limestone"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already disclose safety traits (read-only, open-world, idempotent). The description adds useful behavioral details such as 'capped at 50 results' and 'Keyless' (implying no authentication), which go beyond the annotations. 'Keyless' is somewhat ambiguous but still informative.
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?
The description is short but redundant: 'returning lith_id, name, type, group, and class' is repeated verbatim in the second sentence. The standalone 'Keyless.' is cryptic and could be integrated more clearly. Two sentences with repetition means not every part earns its place.
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 simple tool with one optional parameter and no output schema, the description sufficiently covers usage, return fields, and the result cap. The annotations cover safety, and the description covers functional behavior, making it complete for an agent to invoke correctly.
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?
With 100% schema coverage, the baseline is 3. The description adds value through concrete examples ('granite', 'limestone') for the 'lith' parameter and explains the behavior when omitted, which is more than the schema description provides.
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 clearly states a specific verb+resource: 'Look up Macrostrat lithology definitions by name' and lists the return fields. It distinguishes itself from siblings by focusing solely on lithology definitions, which no sibling tool explicitly covers.
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 provides clear usage context with 'by name' and examples, and explicitly mentions that omitting 'lith' lists all lithologies. However, it does not mention alternatives or exclusions, so it falls short of a 5.
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.
Most tools have distinct purposes, e.g., geology tools vs. Polymarket tools vs. memory tools. The main ambiguity is between ask_pipeworx and ask_pipeworx_grounded, but their descriptions clearly differentiate them (grounded vs. casual). Overall, an agent can reliably select the right tool.
Names use snake_case consistently, but the structure varies: some are verb_noun (find_columns), some are noun_noun (entity_profile), some are single verbs (forget). This mix reduces predictability, though each name is still readable.
With 29 tools, the server covers many domains (geology, finance, prediction markets, memory, subscriptions). This is a large surface for a server named 'Macrostrat', which implies a geology focus. The count feels bloated for a coherent set, though each tool individually seems justified.
The tool set is quite complete for its diverse sub-areas: geology has lookup tools, Pipeworx/Poly market has search, comparison, arbitrage, and memory/subscriptions have full CRUD. Minor gaps exist (e.g., no detailed geology unit edits), but overall coverage is strong.