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topopt

by chartergroup
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Python
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Find a filament

find_material
Read-onlyIdempotent

Look up a 3D printing filament by name to get its modulus, density and print temperatures before setting up a run. Read-only: returns the profile or asks you to pick among candidates.

Instructions

Find a 3D printing filament by name in the catalog that ships with topopt and in the user filaments of the work folder (topopt-filaments.yaml). It reads; it changes nothing.

The user only names the filament; the tool knows the numbers of the data sheet. Use it when the user says which spool they have ("Bambu PLA Basic", "Prusament PETG"), before you ask for a modulus or a density. name: the product as the user says it. Case, punctuation, the maker name and the word "my" do not matter ("polylite abs" and "Polymaker PolyLite ABS" are one filament). A family alone ("PLA", "PETG") names no product: the answer is "ambiguous" with every filament of the family. Returns: ok, status, query, catalog_version, matched_by, origin, profile, derives, candidates, differs, suggestions, makers, user_file, note.

  • "match": profile is the filament: id, name, family, sheet_version, the tensile modulus and strength in the layer plane (xy) and across the layers (z), density, temperatures, enclosure, hardened nozzle, drying, and the source of the values (source_url, opened, tag, field_sources, note). A value that no source states is null (unknown): say so. nu is null for every filament, because no data sheet gives it. derives says what material.filament: <id> sets in a run (E is the lower of the XY and Z modulus, taken from the bending modulus of the sheet when the sheet gives no tensile modulus; density is the one of the sheet, the middle of a range that the sheet gives, or else the median of the filaments of the family, labelled assumed; nu stays the assumed 0.35; a key in the YAML wins) and what the profile cannot give.

  • "ambiguous": candidates and differs (the facts on which they differ). Ask the user which one; do not pick for them.

  • "none": suggestions ("did you mean": never taken by itself) and a note on how to go on: look up the maker data sheet and save_material the values, or ask the user. The numbers of a data sheet describe printed test bars of one maker and one set of print settings, not the part of the user. user_file: path, exists, entries, problems. A problem in the file is a fact; the shipped catalog still works. Next: show the user the derived values. printer_materials shows what the printer of the user can print; check_problem gives the same fit for a YAML with material.filament, in its key material. save_preferences(material={"filament": id}) keeps the filament, or write material: {filament: <id>} in the YAML.

Input Schema

NameRequiredDescriptionDefault
nameYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.5.0

TDQS

A4.9/5.0
Behavior5/5

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

The annotations already declare read-only, non-destructive behavior, but the description adds substantial behavioral context: case/punctuation/maker-name normalization, ambiguous-family handling, `user_file` problem isolation, null-value semantics, derivation rules for `material.filament`, and the caveat that data-sheet values describe printed test bars rather than the user's part. No annotation contradiction is present.

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 definition is front-loaded with purpose and usage, and most content earns its place because there is no output schema and the return statuses are complex. It is still dense prose for a one-parameter lookup, with a long inline listing of return fields that could be more scannable.

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 no output schema and a single undocumented parameter, the description supplies the missing return semantics, statuses, derived-value behavior, user-file behavior, and follow-up tool guidance. Together with annotations covering safety, an agent has enough information to call and interpret the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 0% description coverage for the single `name` parameter, so the description must carry the full burden, and it does. It explains that the product is taken as the user says it, that case, punctuation, maker name and 'my' do not matter, and that a family alone yields an 'ambiguous' result.

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 states a specific verb and resource: it finds a 3D printing filament by name in the shipped catalog and the user's `topopt-filaments.yaml`. It also distinguishes the tool from write operations by saying 'It reads; it changes nothing' and later names related tools such as `save_material`, `printer_materials`, and `check_problem`.

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?

It gives an explicit trigger: 'Use it when the user says which spool they have ... before you ask for a modulus or a density.' It also explains what to do on ambiguity ('Ask the user which one; do not pick for them') and on no match (look up the data sheet and `save_material`, or ask the user), so when-to-use and next alternatives are clear.

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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