MWONGOZO wa Kiufundi
Zana za Kubuni kwa Mawakala wa LLM
Designing tools for LLM agents means writing the names, descriptions, parameter schemas, outputs and error messages that a language model reads to decide which function to call and how to call it.
Katika ukurasa huu4 dakika kusoma
Muhtasari
The model never sees your code, only this text, so a vague description or a sloppy schema produces wrong calls just as reliably as a bug does.
Dive ya kina
A tool definition has four parts the model depends on: a name, a natural-language description, an input schema (usually JSON Schema), and the text the tool sends back. Each works like a prompt. The model decides whether to call a tool by matching the user's goal to descriptions, and it fills in arguments using parameter names, types, enums and examples. Good names are specific and grouped. Prefixes such as github_create_issue and jira_create_issue keep tools apart when an agent connects to many services. Good descriptions say what the tool does, when to use it, when not to, and what it returns, the way you would brief a new colleague. Parameters should say what they mean: user_id is better than id, and an enum of allowed values beats a free-text string the model has to guess at. Outputs matter as much as inputs. Returning thousands of rows or raw internal IDs fills the context window and hides the information that matters. Better tools return readable fields, paginate or truncate by default, and sometimes offer a concise or detailed mode. Error messages should tell the model what went wrong and what to try next, because the model will read them and act on them. One common misconception is that more tools means a more capable agent. Every definition takes up context on every turn, and overlapping tools make selection harder. Accuracy tends to drop as the tool list grows long, especially when tools look alike. No universal cutoff applies. Many practitioners start to see trouble somewhere in the tens of tools, and the usual fixes are consolidating tools, loading them on demand, or splitting work across specialized agents. Another misconception is that tools should mirror an existing API one-to-one. Agents usually do better with fewer, higher-level tools shaped around the tasks people actually do.
Athari za kimkakati
Gharama na bajeti
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Maamuzi ya wazi zaidi
Elimu ya kiufundi husaidia timu kuchagua safu sahihi, sio tu mpya zaidi.
Udhibiti wa ubora
Chaguo bora za uhandisi hupunguza matukio ya kuaminika katika uzalishaji.
The Future of Designing Tools for LLM Agents
Tool design is becoming its own discipline, with published guidance from model providers and shared conventions spreading through protocols such as the Model Context Protocol. Some platforms can now search for and load tool definitions on demand, which eases the pressure to keep lists short, although descriptions still have to be clear enough to find and choose. Models are also being used to critique and rewrite tool descriptions based on evaluation transcripts. The basic rule is unlikely to change: the model can only use a tool as well as its interface explains it, so clear naming, strict schemas and helpful errors will stay central.
Utekelezaji wa Ulimwengu Halisi
A support agent had two tools, search_orders and get_order. Renaming them orders_search_by_customer and orders_get_by_id and saying in each description when to use the other one cut the number of calls sent to the wrong tool.
A calendar tool that took a free-text date field kept getting 'next Tuesday'. Changing the schema to require an ISO 8601 date string with an example in the description removed a whole class of parsing failures.
A database tool that used to return a raw 'Error 1064' now returns 'Column created_date does not exist. Available date columns: created_at, updated_at.' The agent fixes its query on the next try instead of giving up.
A team exposed 60 near-duplicate endpoint wrappers to one agent. Merging them into a dozen task-level tools, such as schedule_meeting in place of separate list_users, find_free_slots and create_event calls, made the agent faster and cheaper to run.
Hatari & Walinzi
Kuboresha kiwango kimoja kunaweza kuficha udhaifu mkubwa wa mfumo.
Gharama za miundombinu na matengenezo mara nyingi hupunguzwa.
Mapengo ya usalama na uonekanaji yanaweza kukua kadiri mifumo inavyozidi kuwa ngumu.
Ramani ya Utekelezaji
Bainisha muda, ubora na malengo ya gharama kabla ya utekelezaji.
Benchmark chini ya mzigo halisi na hali ya data.
Ufuatiliaji wa ala kwa makosa, kuteleza, na athari za mtumiaji.
Tayarisha njia za urejeshaji na majibu ya matukio kabla ya kuongeza ukubwa.
Endelea Kuchunguza
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Maswali yanayoulizwa mara kwa mara
What is Designing Tools for LLM Agents?
Designing tools for LLM agents means writing the names, descriptions, parameter schemas, outputs and error messages that a language model reads to decide which function to call and how to call it. The model never sees your code, only this text, so a vague description or a sloppy schema produces wrong calls just as reliably as a bug does.
What does the model actually use to decide how to call a tool?
The model never sees the implementation. It works only from the text interface: the name, the description, the input schema and whatever the tool sends back.
Why might an agent use prefixes such as github_create_issue and jira_create_issue?
Namespacing tells the model which service a tool belongs to. That matters when several services offer similar actions.
Which parameter design is most likely to produce correct arguments?
Enums and descriptive names remove guesswork. The model picks from valid options instead of inventing a format.
What makes a good tool error message?
The model reads the error and decides what to do next. A message that names the problem and a fix lets it recover on the following call.
Why can adding many tools reduce agent performance?
Every definition is sent with each request, which uses context. Similar-looking tools also make it harder for the model to pick the right one.
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